Overview

Brought to you by YData

Dataset statistics

Number of variables74
Number of observations1439
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 MiB
Average record size in memory2.7 KiB

Variable types

Numeric25
Categorical48
Boolean1

Alerts

PoolArea has constant value "0.0"Constant
1stFlrSF is highly overall correlated with SalePrice and 1 other fieldsHigh correlation
2ndFlrSF is highly overall correlated with BedroomAbvGr and 2 other fieldsHigh correlation
BedroomAbvGr is highly overall correlated with 2ndFlrSF and 2 other fieldsHigh correlation
BldgType is highly overall correlated with MSSubClassHigh correlation
BsmtCond is highly overall correlated with BsmtFinType1 and 1 other fieldsHigh correlation
BsmtExposure is highly overall correlated with BsmtFinType1 and 1 other fieldsHigh correlation
BsmtFinSF1 is highly overall correlated with BsmtFullBath and 1 other fieldsHigh correlation
BsmtFinType1 is highly overall correlated with BsmtCond and 2 other fieldsHigh correlation
BsmtFinType2 is highly overall correlated with BsmtQualHigh correlation
BsmtFullBath is highly overall correlated with BsmtFinSF1High correlation
BsmtQual is highly overall correlated with BsmtCond and 5 other fieldsHigh correlation
BsmtUnfSF is highly overall correlated with BsmtFinSF1High correlation
ExterQual is highly overall correlated with KitchenQual and 1 other fieldsHigh correlation
Exterior1st is highly overall correlated with Exterior2ndHigh correlation
Exterior2nd is highly overall correlated with Exterior1stHigh correlation
FireplaceQu is highly overall correlated with FireplacesHigh correlation
Fireplaces is highly overall correlated with FireplaceQuHigh correlation
Foundation is highly overall correlated with BsmtQual and 1 other fieldsHigh correlation
GarageArea is highly overall correlated with GarageCars and 4 other fieldsHigh correlation
GarageCars is highly overall correlated with GarageArea and 4 other fieldsHigh correlation
GarageCond is highly overall correlated with GarageCars and 2 other fieldsHigh correlation
GarageFinish is highly overall correlated with GarageArea and 4 other fieldsHigh correlation
GarageQual is highly overall correlated with GarageCars and 2 other fieldsHigh correlation
GarageType is highly overall correlated with GarageCars and 1 other fieldsHigh correlation
GarageYrBlt_Age is highly overall correlated with GarageArea and 3 other fieldsHigh correlation
GrLivArea is highly overall correlated with 2ndFlrSF and 3 other fieldsHigh correlation
HalfBath is highly overall correlated with MSSubClassHigh correlation
HouseStyle is highly overall correlated with MSSubClassHigh correlation
KitchenQual is highly overall correlated with ExterQual and 1 other fieldsHigh correlation
LotArea is highly overall correlated with LotFrontageHigh correlation
LotFrontage is highly overall correlated with LotAreaHigh correlation
MSSubClass is highly overall correlated with BldgType and 2 other fieldsHigh correlation
MSZoning is highly overall correlated with NeighborhoodHigh correlation
Neighborhood is highly overall correlated with MSZoningHigh correlation
OverallQual is highly overall correlated with ExterQual and 1 other fieldsHigh correlation
SalePrice is highly overall correlated with 1stFlrSF and 7 other fieldsHigh correlation
TotRmsAbvGrd is highly overall correlated with 2ndFlrSF and 3 other fieldsHigh correlation
TotalBsmtSF is highly overall correlated with 1stFlrSF and 2 other fieldsHigh correlation
YearBuilt_Age is highly overall correlated with Foundation and 4 other fieldsHigh correlation
YearRemodAdd_Age is highly overall correlated with GarageYrBlt_Age and 2 other fieldsHigh correlation
BsmtHalfBath is highly imbalanced (79.9%)Imbalance
KitchenAbvGr is highly imbalanced (85.9%)Imbalance
MSZoning is highly imbalanced (56.6%)Imbalance
LandContour is highly imbalanced (69.0%)Imbalance
LandSlope is highly imbalanced (80.0%)Imbalance
Condition1 is highly imbalanced (72.0%)Imbalance
Condition2 is highly imbalanced (96.4%)Imbalance
BldgType is highly imbalanced (59.6%)Imbalance
RoofStyle is highly imbalanced (65.8%)Imbalance
RoofMatl is highly imbalanced (94.6%)Imbalance
ExterCond is highly imbalanced (73.1%)Imbalance
BsmtCond is highly imbalanced (72.5%)Imbalance
BsmtFinType2 is highly imbalanced (67.6%)Imbalance
Heating is highly imbalanced (92.7%)Imbalance
CentralAir is highly imbalanced (65.2%)Imbalance
Electrical is highly imbalanced (79.9%)Imbalance
Functional is highly imbalanced (82.0%)Imbalance
GarageQual is highly imbalanced (75.6%)Imbalance
GarageCond is highly imbalanced (77.7%)Imbalance
PavedDrive is highly imbalanced (70.0%)Imbalance
SaleType is highly imbalanced (75.1%)Imbalance
SaleCondition is highly imbalanced (62.8%)Imbalance
MasVnrArea has 848 (58.9%) zerosZeros
BsmtFinSF1 has 463 (32.2%) zerosZeros
BsmtFinSF2 has 1279 (88.9%) zerosZeros
BsmtUnfSF has 115 (8.0%) zerosZeros
TotalBsmtSF has 36 (2.5%) zerosZeros
2ndFlrSF has 819 (56.9%) zerosZeros
LowQualFinSF has 1414 (98.3%) zerosZeros
GarageArea has 79 (5.5%) zerosZeros
WoodDeckSF has 751 (52.2%) zerosZeros
OpenPorchSF has 646 (44.9%) zerosZeros
EnclosedPorch has 1233 (85.7%) zerosZeros
3SsnPorch has 1416 (98.4%) zerosZeros
ScreenPorch has 1325 (92.1%) zerosZeros
MiscVal has 1397 (97.1%) zerosZeros

Reproduction

Analysis started2024-09-29 10:42:39.634924
Analysis finished2024-09-29 10:46:15.082622
Duration3 minutes and 35.45 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

LotFrontage
Real number (ℝ)

HIGH CORRELATION 

Distinct108
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.498672
Minimum21
Maximum182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:15.253203image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile35
Q160
median70.049958
Q379
95-th percentile103
Maximum182
Range161
Interquartile range (IQR)19

Descriptive statistics

Standard deviation19.780523
Coefficient of variation (CV)0.28461728
Kurtosis3.0129017
Mean69.498672
Median Absolute Deviation (MAD)10.049958
Skewness0.62861717
Sum100008.59
Variance391.2691
MonotonicityNot monotonic
2024-09-29T16:16:15.485378image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.04995837 252
 
17.5%
60 141
 
9.8%
70 69
 
4.8%
80 68
 
4.7%
50 57
 
4.0%
75 52
 
3.6%
65 44
 
3.1%
85 40
 
2.8%
78 24
 
1.7%
21 23
 
1.6%
Other values (98) 669
46.5%
ValueCountFrequency (%)
21 23
1.6%
24 19
1.3%
30 6
 
0.4%
32 5
 
0.3%
33 1
 
0.1%
34 10
0.7%
35 9
 
0.6%
36 6
 
0.4%
37 5
 
0.3%
38 1
 
0.1%
ValueCountFrequency (%)
182 1
0.1%
174 1
0.1%
168 1
0.1%
153 1
0.1%
152 1
0.1%
149 1
0.1%
144 1
0.1%
141 1
0.1%
140 1
0.1%
138 1
0.1%

LotArea
Real number (ℝ)

HIGH CORRELATION 

Distinct1055
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10012.879
Minimum1300
Maximum70761
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:15.787118image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3230
Q17500
median9430
Q311500
95-th percentile16740.3
Maximum70761
Range69461
Interquartile range (IQR)4000

Descriptive statistics

Standard deviation5447.9458
Coefficient of variation (CV)0.54409384
Kurtosis29.290596
Mean10012.879
Median Absolute Deviation (MAD)1992
Skewness3.9923722
Sum14408533
Variance29680114
MonotonicityNot monotonic
2024-09-29T16:16:16.150557image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7200 25
 
1.7%
9600 24
 
1.7%
6000 17
 
1.2%
9000 14
 
1.0%
8400 14
 
1.0%
10800 13
 
0.9%
1680 10
 
0.7%
7500 9
 
0.6%
6240 8
 
0.6%
6120 8
 
0.6%
Other values (1045) 1297
90.1%
ValueCountFrequency (%)
1300 1
 
0.1%
1477 1
 
0.1%
1491 1
 
0.1%
1526 1
 
0.1%
1533 2
 
0.1%
1596 1
 
0.1%
1680 10
0.7%
1869 1
 
0.1%
1890 2
 
0.1%
1920 1
 
0.1%
ValueCountFrequency (%)
70761 1
0.1%
57200 1
0.1%
53504 1
0.1%
53227 1
0.1%
53107 1
0.1%
50271 1
0.1%
46589 1
0.1%
45600 1
0.1%
40094 1
0.1%
39104 1
0.1%

MasVnrArea
Real number (ℝ)

ZEROS 

Distinct326
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.49443
Minimum0
Maximum1600
Zeros848
Zeros (%)58.9%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:16.457201image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3164.5
95-th percentile451.1
Maximum1600
Range1600
Interquartile range (IQR)164.5

Descriptive statistics

Standard deviation180.00937
Coefficient of variation (CV)1.7393146
Kurtosis10.343716
Mean103.49443
Median Absolute Deviation (MAD)0
Skewness2.6922506
Sum148928.48
Variance32403.373
MonotonicityNot monotonic
2024-09-29T16:16:16.648764image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 848
58.9%
72 8
 
0.6%
103.6852617 8
 
0.6%
180 8
 
0.6%
108 8
 
0.6%
120 7
 
0.5%
16 7
 
0.5%
200 6
 
0.4%
80 6
 
0.4%
106 6
 
0.4%
Other values (316) 527
36.6%
ValueCountFrequency (%)
0 848
58.9%
1 1
 
0.1%
11 1
 
0.1%
14 1
 
0.1%
16 7
 
0.5%
18 2
 
0.1%
22 1
 
0.1%
24 1
 
0.1%
27 1
 
0.1%
28 1
 
0.1%
ValueCountFrequency (%)
1600 1
0.1%
1378 1
0.1%
1170 1
0.1%
1129 1
0.1%
1115 1
0.1%
1047 1
0.1%
1031 1
0.1%
975 1
0.1%
922 1
0.1%
921 1
0.1%

BsmtFinSF1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct631
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean436.17651
Minimum0
Maximum2260
Zeros463
Zeros (%)32.2%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:16.924894image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median378
Q3706
95-th percentile1261.9
Maximum2260
Range2260
Interquartile range (IQR)706

Descriptive statistics

Standard deviation432.44249
Coefficient of variation (CV)0.99143919
Kurtosis-0.022335285
Mean436.17651
Median Absolute Deviation (MAD)378
Skewness0.78587286
Sum627658
Variance187006.5
MonotonicityNot monotonic
2024-09-29T16:16:17.223444image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 463
32.2%
24 12
 
0.8%
16 9
 
0.6%
936 5
 
0.3%
662 5
 
0.3%
686 5
 
0.3%
616 5
 
0.3%
20 5
 
0.3%
400 4
 
0.3%
442 4
 
0.3%
Other values (621) 922
64.1%
ValueCountFrequency (%)
0 463
32.2%
2 1
 
0.1%
16 9
 
0.6%
20 5
 
0.3%
24 12
 
0.8%
25 1
 
0.1%
27 1
 
0.1%
28 3
 
0.2%
33 1
 
0.1%
35 1
 
0.1%
ValueCountFrequency (%)
2260 1
0.1%
2188 1
0.1%
1904 1
0.1%
1880 1
0.1%
1810 1
0.1%
1767 1
0.1%
1721 1
0.1%
1696 1
0.1%
1646 1
0.1%
1636 1
0.1%

BsmtFinSF2
Real number (ℝ)

ZEROS 

Distinct140
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.569145
Minimum0
Maximum1474
Zeros1279
Zeros (%)88.9%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:17.457493image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile391.5
Maximum1474
Range1474
Interquartile range (IQR)0

Descriptive statistics

Standard deviation160.35739
Coefficient of variation (CV)3.5189905
Kurtosis20.699971
Mean45.569145
Median Absolute Deviation (MAD)0
Skewness4.3155232
Sum65574
Variance25714.492
MonotonicityNot monotonic
2024-09-29T16:16:17.701345image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1279
88.9%
180 4
 
0.3%
374 3
 
0.2%
290 2
 
0.1%
64 2
 
0.1%
117 2
 
0.1%
468 2
 
0.1%
182 2
 
0.1%
480 2
 
0.1%
551 2
 
0.1%
Other values (130) 139
 
9.7%
ValueCountFrequency (%)
0 1279
88.9%
28 1
 
0.1%
32 1
 
0.1%
35 1
 
0.1%
40 1
 
0.1%
41 2
 
0.1%
64 2
 
0.1%
68 1
 
0.1%
80 1
 
0.1%
81 1
 
0.1%
ValueCountFrequency (%)
1474 1
0.1%
1127 1
0.1%
1120 1
0.1%
1085 1
0.1%
1080 1
0.1%
1063 1
0.1%
1061 1
0.1%
1057 1
0.1%
1031 1
0.1%
1029 1
0.1%

BsmtUnfSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct777
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean569.74357
Minimum0
Maximum2336
Zeros115
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:17.905725image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1226
median482
Q3811
95-th percentile1468.2
Maximum2336
Range2336
Interquartile range (IQR)585

Descriptive statistics

Standard deviation442.53922
Coefficient of variation (CV)0.77673403
Kurtosis0.46349659
Mean569.74357
Median Absolute Deviation (MAD)290
Skewness0.91607135
Sum819861
Variance195840.96
MonotonicityNot monotonic
2024-09-29T16:16:18.143201image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115
 
8.0%
728 9
 
0.6%
384 8
 
0.6%
600 7
 
0.5%
572 7
 
0.5%
280 6
 
0.4%
625 6
 
0.4%
440 6
 
0.4%
270 6
 
0.4%
300 6
 
0.4%
Other values (767) 1263
87.8%
ValueCountFrequency (%)
0 115
8.0%
14 1
 
0.1%
15 1
 
0.1%
23 2
 
0.1%
26 1
 
0.1%
29 1
 
0.1%
30 1
 
0.1%
32 2
 
0.1%
35 1
 
0.1%
36 4
 
0.3%
ValueCountFrequency (%)
2336 1
0.1%
2153 1
0.1%
2121 1
0.1%
2046 1
0.1%
2042 1
0.1%
2002 1
0.1%
1969 1
0.1%
1935 1
0.1%
1926 1
0.1%
1907 1
0.1%

TotalBsmtSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct713
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1051.4892
Minimum0
Maximum3206
Zeros36
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:18.417989image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile518.6
Q1795
median990
Q31287
95-th percentile1746.6
Maximum3206
Range3206
Interquartile range (IQR)492

Descriptive statistics

Standard deviation416.30001
Coefficient of variation (CV)0.39591467
Kurtosis2.0724288
Mean1051.4892
Median Absolute Deviation (MAD)231
Skewness0.57988084
Sum1513093
Variance173305.7
MonotonicityNot monotonic
2024-09-29T16:16:18.703252image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
 
2.5%
864 35
 
2.4%
672 16
 
1.1%
912 15
 
1.0%
1040 13
 
0.9%
816 13
 
0.9%
768 12
 
0.8%
728 12
 
0.8%
894 11
 
0.8%
848 11
 
0.8%
Other values (703) 1265
87.9%
ValueCountFrequency (%)
0 36
2.5%
105 1
 
0.1%
190 1
 
0.1%
264 3
 
0.2%
270 1
 
0.1%
290 1
 
0.1%
319 1
 
0.1%
360 1
 
0.1%
372 1
 
0.1%
384 7
 
0.5%
ValueCountFrequency (%)
3206 1
0.1%
3200 1
0.1%
3138 1
0.1%
3094 1
0.1%
2633 1
0.1%
2524 1
0.1%
2444 1
0.1%
2392 1
0.1%
2330 1
0.1%
2223 1
0.1%

1stFlrSF
Real number (ℝ)

HIGH CORRELATION 

Distinct744
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1156.6567
Minimum334
Maximum3228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:18.860124image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile675.7
Q1881
median1080
Q31383
95-th percentile1812.5
Maximum3228
Range2894
Interquartile range (IQR)502

Descriptive statistics

Standard deviation373.05624
Coefficient of variation (CV)0.32252978
Kurtosis1.5399348
Mean1156.6567
Median Absolute Deviation (MAD)232
Skewness0.96784995
Sum1664429
Variance139170.96
MonotonicityNot monotonic
2024-09-29T16:16:19.118679image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 25
 
1.7%
1040 16
 
1.1%
912 14
 
1.0%
848 12
 
0.8%
894 12
 
0.8%
672 10
 
0.7%
630 9
 
0.6%
816 9
 
0.6%
483 7
 
0.5%
936 7
 
0.5%
Other values (734) 1318
91.6%
ValueCountFrequency (%)
334 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
483 7
0.5%
495 1
 
0.1%
520 5
0.3%
525 1
 
0.1%
526 1
 
0.1%
536 1
 
0.1%
546 3
0.2%
ValueCountFrequency (%)
3228 1
0.1%
3138 1
0.1%
2898 1
0.1%
2633 1
0.1%
2524 1
0.1%
2515 1
0.1%
2444 1
0.1%
2402 1
0.1%
2392 1
0.1%
2364 1
0.1%

2ndFlrSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct408
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean344.66018
Minimum0
Maximum1872
Zeros819
Zeros (%)56.9%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:19.308878image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3728
95-th percentile1129.5
Maximum1872
Range1872
Interquartile range (IQR)728

Descriptive statistics

Standard deviation432.92413
Coefficient of variation (CV)1.2560898
Kurtosis-0.66305694
Mean344.66018
Median Absolute Deviation (MAD)0
Skewness0.78940843
Sum495966
Variance187423.3
MonotonicityNot monotonic
2024-09-29T16:16:19.546938image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 819
56.9%
728 10
 
0.7%
504 9
 
0.6%
672 8
 
0.6%
546 8
 
0.6%
600 7
 
0.5%
720 6
 
0.4%
896 6
 
0.4%
862 5
 
0.3%
840 5
 
0.3%
Other values (398) 556
38.6%
ValueCountFrequency (%)
0 819
56.9%
110 1
 
0.1%
192 1
 
0.1%
208 1
 
0.1%
213 1
 
0.1%
220 1
 
0.1%
224 1
 
0.1%
240 2
 
0.1%
252 1
 
0.1%
272 1
 
0.1%
ValueCountFrequency (%)
1872 1
0.1%
1818 1
0.1%
1796 1
0.1%
1611 1
0.1%
1589 1
0.1%
1540 1
0.1%
1538 1
0.1%
1523 1
0.1%
1519 1
0.1%
1518 1
0.1%

LowQualFinSF
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.657401
Minimum0
Maximum572
Zeros1414
Zeros (%)98.3%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:19.747506image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum572
Range572
Interquartile range (IQR)0

Descriptive statistics

Standard deviation47.90118
Coefficient of variation (CV)8.4669939
Kurtosis87.089855
Mean5.657401
Median Absolute Deviation (MAD)0
Skewness9.2096906
Sum8141
Variance2294.523
MonotonicityNot monotonic
2024-09-29T16:16:19.994968image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 1414
98.3%
80 3
 
0.2%
360 2
 
0.1%
515 1
 
0.1%
205 1
 
0.1%
479 1
 
0.1%
397 1
 
0.1%
514 1
 
0.1%
120 1
 
0.1%
481 1
 
0.1%
Other values (13) 13
 
0.9%
ValueCountFrequency (%)
0 1414
98.3%
53 1
 
0.1%
80 3
 
0.2%
120 1
 
0.1%
144 1
 
0.1%
156 1
 
0.1%
205 1
 
0.1%
232 1
 
0.1%
234 1
 
0.1%
360 2
 
0.1%
ValueCountFrequency (%)
572 1
0.1%
528 1
0.1%
515 1
0.1%
514 1
0.1%
513 1
0.1%
481 1
0.1%
479 1
0.1%
473 1
0.1%
420 1
0.1%
397 1
0.1%

GrLivArea
Real number (ℝ)

HIGH CORRELATION 

Distinct850
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1506.9743
Minimum334
Maximum4676
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:20.317712image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11128
median1458
Q31769.5
95-th percentile2450.2
Maximum4676
Range4342
Interquartile range (IQR)641.5

Descriptive statistics

Standard deviation505.96691
Coefficient of variation (CV)0.33575019
Kurtosis2.3546521
Mean1506.9743
Median Absolute Deviation (MAD)322
Skewness1.0412736
Sum2168536
Variance256002.51
MonotonicityNot monotonic
2024-09-29T16:16:20.534408image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 22
 
1.5%
1040 14
 
1.0%
894 11
 
0.8%
848 10
 
0.7%
1456 10
 
0.7%
912 9
 
0.6%
1200 8
 
0.6%
816 8
 
0.6%
1092 7
 
0.5%
1728 7
 
0.5%
Other values (840) 1333
92.6%
ValueCountFrequency (%)
334 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
520 1
 
0.1%
605 1
 
0.1%
616 1
 
0.1%
630 6
0.4%
672 2
 
0.1%
691 1
 
0.1%
693 1
 
0.1%
ValueCountFrequency (%)
4676 1
0.1%
4316 1
0.1%
3627 1
0.1%
3608 1
0.1%
3493 1
0.1%
3447 1
0.1%
3395 1
0.1%
3279 1
0.1%
3238 1
0.1%
3228 1
0.1%

BsmtFullBath
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size95.6 KiB
0.0
848 
1.0
579 
2.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4317
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 848
58.9%
1.0 579
40.2%
2.0 12
 
0.8%

Length

2024-09-29T16:16:20.740111image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:20.900797image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 848
58.9%
1.0 579
40.2%
2.0 12
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 2287
53.0%
. 1439
33.3%
1 579
 
13.4%
2 12
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2287
53.0%
. 1439
33.3%
1 579
 
13.4%
2 12
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2287
53.0%
. 1439
33.3%
1 579
 
13.4%
2 12
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2287
53.0%
. 1439
33.3%
1 579
 
13.4%
2 12
 
0.3%

BsmtHalfBath
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size95.6 KiB
0.0
1359 
1.0
 
78
2.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4317
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1359
94.4%
1.0 78
 
5.4%
2.0 2
 
0.1%

Length

2024-09-29T16:16:21.053108image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:21.212999image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1359
94.4%
1.0 78
 
5.4%
2.0 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2798
64.8%
. 1439
33.3%
1 78
 
1.8%
2 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2798
64.8%
. 1439
33.3%
1 78
 
1.8%
2 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2798
64.8%
. 1439
33.3%
1 78
 
1.8%
2 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2798
64.8%
. 1439
33.3%
1 78
 
1.8%
2 2
 
< 0.1%

FullBath
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size95.6 KiB
2.0
755 
1.0
645 
3.0
 
30
0.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4317
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 755
52.5%
1.0 645
44.8%
3.0 30
 
2.1%
0.0 9
 
0.6%

Length

2024-09-29T16:16:21.373251image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:21.520807image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 755
52.5%
1.0 645
44.8%
3.0 30
 
2.1%
0.0 9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1448
33.5%
. 1439
33.3%
2 755
17.5%
1 645
14.9%
3 30
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1448
33.5%
. 1439
33.3%
2 755
17.5%
1 645
14.9%
3 30
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1448
33.5%
. 1439
33.3%
2 755
17.5%
1 645
14.9%
3 30
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1448
33.5%
. 1439
33.3%
2 755
17.5%
1 645
14.9%
3 30
 
0.7%

HalfBath
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size95.6 KiB
0.0
897 
1.0
531 
2.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4317
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 897
62.3%
1.0 531
36.9%
2.0 11
 
0.8%

Length

2024-09-29T16:16:21.709846image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:21.823787image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 897
62.3%
1.0 531
36.9%
2.0 11
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 2336
54.1%
. 1439
33.3%
1 531
 
12.3%
2 11
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2336
54.1%
. 1439
33.3%
1 531
 
12.3%
2 11
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2336
54.1%
. 1439
33.3%
1 531
 
12.3%
2 11
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2336
54.1%
. 1439
33.3%
1 531
 
12.3%
2 11
 
0.3%

BedroomAbvGr
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8603197
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:21.931214image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81490132
Coefficient of variation (CV)0.28489869
Kurtosis2.2828253
Mean2.8603197
Median Absolute Deviation (MAD)0
Skewness0.2150003
Sum4116
Variance0.66406415
MonotonicityNot monotonic
2024-09-29T16:16:22.100187image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 794
55.2%
2 355
24.7%
4 206
 
14.3%
1 50
 
3.5%
5 20
 
1.4%
6 7
 
0.5%
0 6
 
0.4%
8 1
 
0.1%
ValueCountFrequency (%)
0 6
 
0.4%
1 50
 
3.5%
2 355
24.7%
3 794
55.2%
4 206
 
14.3%
5 20
 
1.4%
6 7
 
0.5%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
6 7
 
0.5%
5 20
 
1.4%
4 206
 
14.3%
3 794
55.2%
2 355
24.7%
1 50
 
3.5%
0 6
 
0.4%

KitchenAbvGr
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size95.6 KiB
1.0
1373 
2.0
 
63
3.0
 
2
0.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4317
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1373
95.4%
2.0 63
 
4.4%
3.0 2
 
0.1%
0.0 1
 
0.1%

Length

2024-09-29T16:16:22.411146image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:22.621424image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1373
95.4%
2.0 63
 
4.4%
3.0 2
 
0.1%
0.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1440
33.4%
. 1439
33.3%
1 1373
31.8%
2 63
 
1.5%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1440
33.4%
. 1439
33.3%
1 1373
31.8%
2 63
 
1.5%
3 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1440
33.4%
. 1439
33.3%
1 1373
31.8%
2 63
 
1.5%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1440
33.4%
. 1439
33.3%
1 1373
31.8%
2 63
 
1.5%
3 2
 
< 0.1%

TotRmsAbvGrd
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5031272
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:22.776471image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile10
Maximum14
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6083354
Coefficient of variation (CV)0.24731722
Kurtosis0.82027871
Mean6.5031272
Median Absolute Deviation (MAD)1
Skewness0.64212524
Sum9358
Variance2.5867426
MonotonicityNot monotonic
2024-09-29T16:16:22.907470image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 398
27.7%
7 326
22.7%
5 270
18.8%
8 184
12.8%
4 97
 
6.7%
9 73
 
5.1%
10 46
 
3.2%
11 18
 
1.3%
3 17
 
1.2%
12 8
 
0.6%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
2 1
 
0.1%
3 17
 
1.2%
4 97
 
6.7%
5 270
18.8%
6 398
27.7%
7 326
22.7%
8 184
12.8%
9 73
 
5.1%
10 46
 
3.2%
11 18
 
1.3%
ValueCountFrequency (%)
14 1
 
0.1%
12 8
 
0.6%
11 18
 
1.3%
10 46
 
3.2%
9 73
 
5.1%
8 184
12.8%
7 326
22.7%
6 398
27.7%
5 270
18.8%
4 97
 
6.7%

Fireplaces
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size95.6 KiB
0.0
686 
1.0
642 
2.0
107 
3.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4317
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 686
47.7%
1.0 642
44.6%
2.0 107
 
7.4%
3.0 4
 
0.3%

Length

2024-09-29T16:16:23.072541image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:23.246929image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 686
47.7%
1.0 642
44.6%
2.0 107
 
7.4%
3.0 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 2125
49.2%
. 1439
33.3%
1 642
 
14.9%
2 107
 
2.5%
3 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2125
49.2%
. 1439
33.3%
1 642
 
14.9%
2 107
 
2.5%
3 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2125
49.2%
. 1439
33.3%
1 642
 
14.9%
2 107
 
2.5%
3 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2125
49.2%
. 1439
33.3%
1 642
 
14.9%
2 107
 
2.5%
3 4
 
0.1%

GarageCars
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size95.6 KiB
2.0
810 
1.0
365 
3.0
180 
0.0
 
79
4.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4317
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.0 810
56.3%
1.0 365
25.4%
3.0 180
 
12.5%
0.0 79
 
5.5%
4.0 5
 
0.3%

Length

2024-09-29T16:16:23.417041image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:24.679121image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 810
56.3%
1.0 365
25.4%
3.0 180
 
12.5%
0.0 79
 
5.5%
4.0 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1518
35.2%
. 1439
33.3%
2 810
18.8%
1 365
 
8.5%
3 180
 
4.2%
4 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1518
35.2%
. 1439
33.3%
2 810
18.8%
1 365
 
8.5%
3 180
 
4.2%
4 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1518
35.2%
. 1439
33.3%
2 810
18.8%
1 365
 
8.5%
3 180
 
4.2%
4 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1518
35.2%
. 1439
33.3%
2 810
18.8%
1 365
 
8.5%
3 180
 
4.2%
4 5
 
0.1%

GarageArea
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct436
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.42877
Minimum0
Maximum1390
Zeros79
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:25.064020image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1333
median478
Q3576
95-th percentile850.2
Maximum1390
Range1390
Interquartile range (IQR)243

Descriptive statistics

Standard deviation212.35895
Coefficient of variation (CV)0.44950468
Kurtosis0.76497283
Mean472.42877
Median Absolute Deviation (MAD)118
Skewness0.1424438
Sum679825
Variance45096.322
MonotonicityNot monotonic
2024-09-29T16:16:25.365252image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 79
 
5.5%
440 49
 
3.4%
576 46
 
3.2%
240 38
 
2.6%
484 33
 
2.3%
528 33
 
2.3%
288 26
 
1.8%
400 25
 
1.7%
264 24
 
1.7%
480 24
 
1.7%
Other values (426) 1062
73.8%
ValueCountFrequency (%)
0 79
5.5%
160 2
 
0.1%
164 1
 
0.1%
180 9
 
0.6%
186 1
 
0.1%
189 1
 
0.1%
192 1
 
0.1%
198 1
 
0.1%
200 4
 
0.3%
205 3
 
0.2%
ValueCountFrequency (%)
1390 1
0.1%
1356 1
0.1%
1248 1
0.1%
1220 1
0.1%
1166 1
0.1%
1134 1
0.1%
1069 1
0.1%
1053 1
0.1%
1052 2
0.1%
1043 1
0.1%

WoodDeckSF
Real number (ℝ)

ZEROS 

Distinct270
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.348853
Minimum0
Maximum857
Zeros751
Zeros (%)52.2%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:25.725703image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3168
95-th percentile328.5
Maximum857
Range857
Interquartile range (IQR)168

Descriptive statistics

Standard deviation123.82958
Coefficient of variation (CV)1.3265249
Kurtosis2.8924149
Mean93.348853
Median Absolute Deviation (MAD)0
Skewness1.516568
Sum134329
Variance15333.766
MonotonicityNot monotonic
2024-09-29T16:16:26.050832image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 751
52.2%
192 38
 
2.6%
100 36
 
2.5%
144 33
 
2.3%
120 30
 
2.1%
168 28
 
1.9%
140 15
 
1.0%
224 14
 
1.0%
240 10
 
0.7%
208 10
 
0.7%
Other values (260) 474
32.9%
ValueCountFrequency (%)
0 751
52.2%
12 2
 
0.1%
24 2
 
0.1%
26 2
 
0.1%
28 2
 
0.1%
30 1
 
0.1%
32 1
 
0.1%
33 1
 
0.1%
35 1
 
0.1%
36 4
 
0.3%
ValueCountFrequency (%)
857 1
0.1%
736 1
0.1%
728 1
0.1%
668 1
0.1%
635 1
0.1%
576 1
0.1%
574 1
0.1%
550 1
0.1%
536 1
0.1%
519 1
0.1%

OpenPorchSF
Real number (ℝ)

ZEROS 

Distinct200
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.618485
Minimum0
Maximum547
Zeros646
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:26.303589image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q368
95-th percentile174.1
Maximum547
Range547
Interquartile range (IQR)68

Descriptive statistics

Standard deviation66.070355
Coefficient of variation (CV)1.4172566
Kurtosis8.6139231
Mean46.618485
Median Absolute Deviation (MAD)25
Skewness2.37053
Sum67084
Variance4365.2918
MonotonicityNot monotonic
2024-09-29T16:16:26.660594image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 646
44.9%
36 29
 
2.0%
20 21
 
1.5%
48 21
 
1.5%
45 19
 
1.3%
40 19
 
1.3%
30 16
 
1.1%
24 15
 
1.0%
39 14
 
1.0%
28 14
 
1.0%
Other values (190) 625
43.4%
ValueCountFrequency (%)
0 646
44.9%
4 1
 
0.1%
8 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 3
 
0.2%
15 1
 
0.1%
16 7
 
0.5%
17 2
 
0.1%
18 5
 
0.3%
ValueCountFrequency (%)
547 1
0.1%
523 1
0.1%
502 1
0.1%
418 1
0.1%
406 1
0.1%
364 1
0.1%
341 1
0.1%
319 1
0.1%
312 2
0.1%
304 1
0.1%

EnclosedPorch
Real number (ℝ)

ZEROS 

Distinct118
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.790132
Minimum0
Maximum386
Zeros1233
Zeros (%)85.7%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:27.025652image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile180.3
Maximum386
Range386
Interquartile range (IQR)0

Descriptive statistics

Standard deviation59.81475
Coefficient of variation (CV)2.7450384
Kurtosis7.5691673
Mean21.790132
Median Absolute Deviation (MAD)0
Skewness2.8629179
Sum31356
Variance3577.8043
MonotonicityNot monotonic
2024-09-29T16:16:27.282613image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1233
85.7%
112 15
 
1.0%
96 6
 
0.4%
192 5
 
0.3%
144 5
 
0.3%
216 5
 
0.3%
120 5
 
0.3%
156 4
 
0.3%
116 4
 
0.3%
252 4
 
0.3%
Other values (108) 153
 
10.6%
ValueCountFrequency (%)
0 1233
85.7%
19 1
 
0.1%
20 1
 
0.1%
24 1
 
0.1%
30 1
 
0.1%
32 2
 
0.1%
34 2
 
0.1%
36 2
 
0.1%
37 1
 
0.1%
39 2
 
0.1%
ValueCountFrequency (%)
386 1
0.1%
330 1
0.1%
318 1
0.1%
301 1
0.1%
294 1
0.1%
293 1
0.1%
291 1
0.1%
286 1
0.1%
280 1
0.1%
275 1
0.1%

3SsnPorch
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1063238
Minimum0
Maximum407
Zeros1416
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:27.515832image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum407
Range407
Interquartile range (IQR)0

Descriptive statistics

Standard deviation26.358008
Coefficient of variation (CV)8.4852738
Kurtosis100.35805
Mean3.1063238
Median Absolute Deviation (MAD)0
Skewness9.5356135
Sum4470
Variance694.7446
MonotonicityNot monotonic
2024-09-29T16:16:27.717892image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 1416
98.4%
168 3
 
0.2%
180 2
 
0.1%
216 2
 
0.1%
144 2
 
0.1%
182 1
 
0.1%
290 1
 
0.1%
153 1
 
0.1%
96 1
 
0.1%
23 1
 
0.1%
Other values (9) 9
 
0.6%
ValueCountFrequency (%)
0 1416
98.4%
23 1
 
0.1%
96 1
 
0.1%
130 1
 
0.1%
140 1
 
0.1%
144 2
 
0.1%
153 1
 
0.1%
162 1
 
0.1%
168 3
 
0.2%
180 2
 
0.1%
ValueCountFrequency (%)
407 1
0.1%
320 1
0.1%
304 1
0.1%
290 1
0.1%
245 1
0.1%
238 1
0.1%
216 2
0.1%
196 1
0.1%
182 1
0.1%
180 2
0.1%

ScreenPorch
Real number (ℝ)

ZEROS 

Distinct75
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.858235
Minimum0
Maximum480
Zeros1325
Zeros (%)92.1%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:27.950337image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile160
Maximum480
Range480
Interquartile range (IQR)0

Descriptive statistics

Standard deviation54.858125
Coefficient of variation (CV)3.6921024
Kurtosis17.719487
Mean14.858235
Median Absolute Deviation (MAD)0
Skewness4.0641946
Sum21381
Variance3009.4138
MonotonicityNot monotonic
2024-09-29T16:16:28.162948image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1325
92.1%
192 6
 
0.4%
120 5
 
0.3%
224 5
 
0.3%
189 4
 
0.3%
180 4
 
0.3%
144 3
 
0.2%
147 3
 
0.2%
126 3
 
0.2%
90 3
 
0.2%
Other values (65) 78
 
5.4%
ValueCountFrequency (%)
0 1325
92.1%
40 1
 
0.1%
53 1
 
0.1%
60 1
 
0.1%
63 1
 
0.1%
80 1
 
0.1%
90 3
 
0.2%
95 1
 
0.1%
99 1
 
0.1%
100 2
 
0.1%
ValueCountFrequency (%)
480 1
0.1%
410 1
0.1%
396 1
0.1%
385 1
0.1%
374 1
0.1%
322 1
0.1%
312 1
0.1%
291 1
0.1%
288 2
0.1%
287 1
0.1%

PoolArea
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size95.6 KiB
0.0
1439 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4317
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1439
100.0%

Length

2024-09-29T16:16:28.340556image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:28.488534image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1439
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2878
66.7%
. 1439
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2878
66.7%
. 1439
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2878
66.7%
. 1439
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2878
66.7%
. 1439
33.3%

MiscVal
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.021543
Minimum0
Maximum1400
Zeros1397
Zeros (%)97.1%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:28.726168image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1400
Range1400
Interquartile range (IQR)0

Descriptive statistics

Standard deviation108.98377
Coefficient of variation (CV)6.4026963
Kurtosis69.867286
Mean17.021543
Median Absolute Deviation (MAD)0
Skewness7.7512434
Sum24494
Variance11877.462
MonotonicityNot monotonic
2024-09-29T16:16:28.947652image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 1397
97.1%
400 11
 
0.8%
500 7
 
0.5%
700 4
 
0.3%
450 4
 
0.3%
600 4
 
0.3%
480 2
 
0.1%
1200 2
 
0.1%
350 1
 
0.1%
800 1
 
0.1%
Other values (6) 6
 
0.4%
ValueCountFrequency (%)
0 1397
97.1%
54 1
 
0.1%
350 1
 
0.1%
400 11
 
0.8%
450 4
 
0.3%
480 2
 
0.1%
500 7
 
0.5%
560 1
 
0.1%
600 4
 
0.3%
620 1
 
0.1%
ValueCountFrequency (%)
1400 1
 
0.1%
1300 1
 
0.1%
1200 2
 
0.1%
1150 1
 
0.1%
800 1
 
0.1%
700 4
0.3%
620 1
 
0.1%
600 4
0.3%
560 1
 
0.1%
500 7
0.5%

MoSold
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3300903
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:29.181332image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7076114
Coefficient of variation (CV)0.42773661
Kurtosis-0.41525961
Mean6.3300903
Median Absolute Deviation (MAD)2
Skewness0.2148291
Sum9109
Variance7.3311593
MonotonicityNot monotonic
2024-09-29T16:16:29.370449image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 249
17.3%
7 229
15.9%
5 201
14.0%
4 140
9.7%
8 119
8.3%
3 104
7.2%
10 89
 
6.2%
11 78
 
5.4%
9 63
 
4.4%
12 59
 
4.1%
Other values (2) 108
7.5%
ValueCountFrequency (%)
1 56
 
3.9%
2 52
 
3.6%
3 104
7.2%
4 140
9.7%
5 201
14.0%
6 249
17.3%
7 229
15.9%
8 119
8.3%
9 63
 
4.4%
10 89
 
6.2%
ValueCountFrequency (%)
12 59
 
4.1%
11 78
 
5.4%
10 89
 
6.2%
9 63
 
4.4%
8 119
8.3%
7 229
15.9%
6 249
17.3%
5 201
14.0%
4 140
9.7%
3 104
7.2%

SalePrice
Real number (ℝ)

HIGH CORRELATION 

Distinct655
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180070
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:29.570848image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129900
median162000
Q3213125
95-th percentile325661.6
Maximum755000
Range720100
Interquartile range (IQR)83225

Descriptive statistics

Standard deviation78085.403
Coefficient of variation (CV)0.43363916
Kurtosis5.4998787
Mean180070
Median Absolute Deviation (MAD)37500
Skewness1.766223
Sum2.5912073 × 108
Variance6.0973301 × 109
MonotonicityNot monotonic
2024-09-29T16:16:29.801444image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140000 20
 
1.4%
135000 17
 
1.2%
145000 14
 
1.0%
155000 14
 
1.0%
110000 13
 
0.9%
190000 12
 
0.8%
115000 12
 
0.8%
160000 11
 
0.8%
130000 11
 
0.8%
139000 11
 
0.8%
Other values (645) 1304
90.6%
ValueCountFrequency (%)
34900 1
0.1%
35311 1
0.1%
37900 1
0.1%
39300 1
0.1%
40000 1
0.1%
52000 1
0.1%
52500 1
0.1%
55000 1
0.1%
55993 1
0.1%
58500 1
0.1%
ValueCountFrequency (%)
755000 1
0.1%
625000 1
0.1%
611657 1
0.1%
582933 1
0.1%
556581 1
0.1%
555000 1
0.1%
538000 1
0.1%
501837 1
0.1%
485000 1
0.1%
475000 1
0.1%

YearBuilt_Age
Real number (ℝ)

HIGH CORRELATION 

Distinct112
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.680334
Minimum14
Maximum152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:30.015301image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile17
Q123
median51
Q370
95-th percentile108
Maximum152
Range138
Interquartile range (IQR)47

Descriptive statistics

Standard deviation30.302795
Coefficient of variation (CV)0.57522025
Kurtosis-0.44473958
Mean52.680334
Median Absolute Deviation (MAD)25
Skewness0.61712848
Sum75807
Variance918.25936
MonotonicityNot monotonic
2024-09-29T16:16:30.326199image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 67
 
4.7%
19 64
 
4.4%
20 54
 
3.8%
17 49
 
3.4%
21 45
 
3.1%
48 33
 
2.3%
47 30
 
2.1%
104 30
 
2.1%
65 26
 
1.8%
26 25
 
1.7%
Other values (102) 1016
70.6%
ValueCountFrequency (%)
14 1
 
0.1%
15 18
 
1.3%
16 22
 
1.5%
17 49
3.4%
18 67
4.7%
19 64
4.4%
20 54
3.8%
21 45
3.1%
22 23
 
1.6%
23 19
 
1.3%
ValueCountFrequency (%)
152 1
 
0.1%
149 1
 
0.1%
144 4
 
0.3%
142 1
 
0.1%
139 2
 
0.1%
134 2
 
0.1%
132 2
 
0.1%
131 1
 
0.1%
126 1
 
0.1%
124 10
0.7%

YearRemodAdd_Age
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.199444
Minimum14
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:30.671920image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile17
Q120
median30
Q357.5
95-th percentile74
Maximum74
Range60
Interquartile range (IQR)37.5

Descriptive statistics

Standard deviation20.690375
Coefficient of variation (CV)0.52782318
Kurtosis-1.2813266
Mean39.199444
Median Absolute Deviation (MAD)13
Skewness0.49858894
Sum56408
Variance428.09163
MonotonicityNot monotonic
2024-09-29T16:16:30.896513image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 177
 
12.3%
18 94
 
6.5%
17 75
 
5.2%
19 73
 
5.1%
20 62
 
4.3%
24 54
 
3.8%
21 51
 
3.5%
22 46
 
3.2%
16 39
 
2.7%
28 35
 
2.4%
Other values (51) 733
50.9%
ValueCountFrequency (%)
14 6
 
0.4%
15 23
 
1.6%
16 39
2.7%
17 75
5.2%
18 94
6.5%
19 73
5.1%
20 62
4.3%
21 51
3.5%
22 46
3.2%
23 21
 
1.5%
ValueCountFrequency (%)
74 177
12.3%
73 4
 
0.3%
72 5
 
0.3%
71 10
 
0.7%
70 14
 
1.0%
69 9
 
0.6%
68 10
 
0.7%
67 9
 
0.6%
66 15
 
1.0%
65 18
 
1.3%

GarageYrBlt_Age
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.401677
Minimum14
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-09-29T16:16:31.163020image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile17
Q123
median45.493836
Q362
95-th percentile94
Maximum124
Range110
Interquartile range (IQR)39

Descriptive statistics

Standard deviation24.025457
Coefficient of variation (CV)0.52917555
Kurtosis-0.26801229
Mean45.401677
Median Absolute Deviation (MAD)20.493836
Skewness0.67354364
Sum65333.013
Variance577.22259
MonotonicityNot monotonic
2024-09-29T16:16:31.370749image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.49383611 79
 
5.5%
19 65
 
4.5%
18 59
 
4.1%
20 53
 
3.7%
21 49
 
3.4%
17 49
 
3.4%
47 33
 
2.3%
26 31
 
2.2%
25 30
 
2.1%
48 29
 
2.0%
Other values (88) 962
66.9%
ValueCountFrequency (%)
14 3
 
0.2%
15 21
 
1.5%
16 28
1.9%
17 49
3.4%
18 59
4.1%
19 65
4.5%
20 53
3.7%
21 49
3.4%
22 26
 
1.8%
23 19
 
1.3%
ValueCountFrequency (%)
124 1
 
0.1%
118 1
 
0.1%
116 1
 
0.1%
114 3
 
0.2%
110 2
 
0.1%
109 2
 
0.1%
108 5
 
0.3%
106 1
 
0.1%
104 14
1.0%
103 3
 
0.2%

YrSold_Age
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size97.0 KiB
15.0
334 
17.0
323 
18.0
310 
16.0
300 
14.0
172 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5756
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row16.0
2nd row17.0
3rd row16.0
4th row18.0
5th row16.0

Common Values

ValueCountFrequency (%)
15.0 334
23.2%
17.0 323
22.4%
18.0 310
21.5%
16.0 300
20.8%
14.0 172
12.0%

Length

2024-09-29T16:16:31.581134image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:31.782091image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
15.0 334
23.2%
17.0 323
22.4%
18.0 310
21.5%
16.0 300
20.8%
14.0 172
12.0%

Most occurring characters

ValueCountFrequency (%)
1 1439
25.0%
. 1439
25.0%
0 1439
25.0%
5 334
 
5.8%
7 323
 
5.6%
8 310
 
5.4%
6 300
 
5.2%
4 172
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5756
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1439
25.0%
. 1439
25.0%
0 1439
25.0%
5 334
 
5.8%
7 323
 
5.6%
8 310
 
5.4%
6 300
 
5.2%
4 172
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5756
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1439
25.0%
. 1439
25.0%
0 1439
25.0%
5 334
 
5.8%
7 323
 
5.6%
8 310
 
5.4%
6 300
 
5.2%
4 172
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5756
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1439
25.0%
. 1439
25.0%
0 1439
25.0%
5 334
 
5.8%
7 323
 
5.6%
8 310
 
5.4%
6 300
 
5.2%
4 172
 
3.0%

MSSubClass
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size130.7 KiB
1-STORY 1946 & NEWER ALL STYLES
528 
2-STORY 1946 & NEWER
296 
1-1/2 STORY FINISHED ALL AGES
142 
1-STORY PUD (Planned Unit Development) - 1946 & NEWER
87 
1-STORY 1945 & OLDER
69 
Other values (10)
317 

Length

Max length53
Median length41
Mean length28.020153
Min length11

Characters and Unicode

Total characters40321
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2-STORY 1946 & NEWER
2nd row1-STORY 1946 & NEWER ALL STYLES
3rd row2-STORY 1946 & NEWER
4th row2-STORY 1945 & OLDER
5th row2-STORY 1946 & NEWER

Common Values

ValueCountFrequency (%)
1-STORY 1946 & NEWER ALL STYLES 528
36.7%
2-STORY 1946 & NEWER 296
20.6%
1-1/2 STORY FINISHED ALL AGES 142
 
9.9%
1-STORY PUD (Planned Unit Development) - 1946 & NEWER 87
 
6.0%
1-STORY 1945 & OLDER 69
 
4.8%
2-STORY PUD - 1946 & NEWER 63
 
4.4%
2-STORY 1945 & OLDER 59
 
4.1%
SPLIT OR MULTI-LEVEL 56
 
3.9%
DUPLEX - ALL STYLES AND AGES 50
 
3.5%
2 FAMILY CONVERSION - ALL STYLES AND AGES 28
 
1.9%
Other values (5) 61
 
4.2%

Length

2024-09-29T16:16:32.018507image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1362
17.7%
newer 974
12.7%
1946 974
12.7%
all 779
10.1%
1-story 688
8.9%
styles 606
7.9%
2-story 418
 
5.4%
ages 251
 
3.3%
story 169
 
2.2%
pud 160
 
2.1%
Other values (23) 1308
17.0%

Most occurring characters

ValueCountFrequency (%)
6250
15.5%
E 3341
 
8.3%
S 3010
 
7.5%
L 2674
 
6.6%
R 2491
 
6.2%
1 2113
 
5.2%
T 2041
 
5.1%
Y 1939
 
4.8%
- 1591
 
3.9%
O 1545
 
3.8%
Other values (34) 13326
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40321
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6250
15.5%
E 3341
 
8.3%
S 3010
 
7.5%
L 2674
 
6.6%
R 2491
 
6.2%
1 2113
 
5.2%
T 2041
 
5.1%
Y 1939
 
4.8%
- 1591
 
3.9%
O 1545
 
3.8%
Other values (34) 13326
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40321
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6250
15.5%
E 3341
 
8.3%
S 3010
 
7.5%
L 2674
 
6.6%
R 2491
 
6.2%
1 2113
 
5.2%
T 2041
 
5.1%
Y 1939
 
4.8%
- 1591
 
3.9%
O 1545
 
3.8%
Other values (34) 13326
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40321
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6250
15.5%
E 3341
 
8.3%
S 3010
 
7.5%
L 2674
 
6.6%
R 2491
 
6.2%
1 2113
 
5.2%
T 2041
 
5.1%
Y 1939
 
4.8%
- 1591
 
3.9%
O 1545
 
3.8%
Other values (34) 13326
33.0%

MSZoning
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size94.2 KiB
RL
1131 
RM
217 
FV
 
65
RH
 
16
C (all)
 
10

Length

Max length7
Median length2
Mean length2.0347464
Min length2

Characters and Unicode

Total characters2928
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRL
2nd rowRL
3rd rowRL
4th rowRL
5th rowRL

Common Values

ValueCountFrequency (%)
RL 1131
78.6%
RM 217
 
15.1%
FV 65
 
4.5%
RH 16
 
1.1%
C (all) 10
 
0.7%

Length

2024-09-29T16:16:32.303744image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:32.498019image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
rl 1131
78.1%
rm 217
 
15.0%
fv 65
 
4.5%
rh 16
 
1.1%
c 10
 
0.7%
all 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
R 1364
46.6%
L 1131
38.6%
M 217
 
7.4%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1364
46.6%
L 1131
38.6%
M 217
 
7.4%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1364
46.6%
L 1131
38.6%
M 217
 
7.4%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1364
46.6%
L 1131
38.6%
M 217
 
7.4%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

LotShape
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size95.6 KiB
Reg
915 
IR1
478 
IR2
 
38
IR3
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4317
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReg
2nd rowReg
3rd rowIR1
4th rowIR1
5th rowIR1

Common Values

ValueCountFrequency (%)
Reg 915
63.6%
IR1 478
33.2%
IR2 38
 
2.6%
IR3 8
 
0.6%

Length

2024-09-29T16:16:32.716839image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:32.852770image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
reg 915
63.6%
ir1 478
33.2%
ir2 38
 
2.6%
ir3 8
 
0.6%

Most occurring characters

ValueCountFrequency (%)
R 1439
33.3%
e 915
21.2%
g 915
21.2%
I 524
 
12.1%
1 478
 
11.1%
2 38
 
0.9%
3 8
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1439
33.3%
e 915
21.2%
g 915
21.2%
I 524
 
12.1%
1 478
 
11.1%
2 38
 
0.9%
3 8
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1439
33.3%
e 915
21.2%
g 915
21.2%
I 524
 
12.1%
1 478
 
11.1%
2 38
 
0.9%
3 8
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1439
33.3%
e 915
21.2%
g 915
21.2%
I 524
 
12.1%
1 478
 
11.1%
2 38
 
0.9%
3 8
 
0.2%

LandContour
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size95.6 KiB
Lvl
1296 
Bnk
 
62
HLS
 
48
Low
 
33

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4317
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLvl
2nd rowLvl
3rd rowLvl
4th rowLvl
5th rowLvl

Common Values

ValueCountFrequency (%)
Lvl 1296
90.1%
Bnk 62
 
4.3%
HLS 48
 
3.3%
Low 33
 
2.3%

Length

2024-09-29T16:16:33.052126image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:33.241679image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
lvl 1296
90.1%
bnk 62
 
4.3%
hls 48
 
3.3%
low 33
 
2.3%

Most occurring characters

ValueCountFrequency (%)
L 1377
31.9%
v 1296
30.0%
l 1296
30.0%
B 62
 
1.4%
n 62
 
1.4%
k 62
 
1.4%
H 48
 
1.1%
S 48
 
1.1%
o 33
 
0.8%
w 33
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 1377
31.9%
v 1296
30.0%
l 1296
30.0%
B 62
 
1.4%
n 62
 
1.4%
k 62
 
1.4%
H 48
 
1.1%
S 48
 
1.1%
o 33
 
0.8%
w 33
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 1377
31.9%
v 1296
30.0%
l 1296
30.0%
B 62
 
1.4%
n 62
 
1.4%
k 62
 
1.4%
H 48
 
1.1%
S 48
 
1.1%
o 33
 
0.8%
w 33
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 1377
31.9%
v 1296
30.0%
l 1296
30.0%
B 62
 
1.4%
n 62
 
1.4%
k 62
 
1.4%
H 48
 
1.1%
S 48
 
1.1%
o 33
 
0.8%
w 33
 
0.8%

LotConfig
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size99.7 KiB
Inside
1041 
Corner
257 
CulDSac
 
90
FR2
 
47
FR3
 
4

Length

Max length7
Median length6
Mean length5.9562196
Min length3

Characters and Unicode

Total characters8571
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInside
2nd rowFR2
3rd rowInside
4th rowCorner
5th rowFR2

Common Values

ValueCountFrequency (%)
Inside 1041
72.3%
Corner 257
 
17.9%
CulDSac 90
 
6.3%
FR2 47
 
3.3%
FR3 4
 
0.3%

Length

2024-09-29T16:16:33.505079image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:33.737581image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
inside 1041
72.3%
corner 257
 
17.9%
culdsac 90
 
6.3%
fr2 47
 
3.3%
fr3 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 1298
15.1%
n 1298
15.1%
I 1041
12.1%
s 1041
12.1%
i 1041
12.1%
d 1041
12.1%
r 514
 
6.0%
C 347
 
4.0%
o 257
 
3.0%
S 90
 
1.1%
Other values (9) 603
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8571
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1298
15.1%
n 1298
15.1%
I 1041
12.1%
s 1041
12.1%
i 1041
12.1%
d 1041
12.1%
r 514
 
6.0%
C 347
 
4.0%
o 257
 
3.0%
S 90
 
1.1%
Other values (9) 603
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8571
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1298
15.1%
n 1298
15.1%
I 1041
12.1%
s 1041
12.1%
i 1041
12.1%
d 1041
12.1%
r 514
 
6.0%
C 347
 
4.0%
o 257
 
3.0%
S 90
 
1.1%
Other values (9) 603
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8571
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1298
15.1%
n 1298
15.1%
I 1041
12.1%
s 1041
12.1%
i 1041
12.1%
d 1041
12.1%
r 514
 
6.0%
C 347
 
4.0%
o 257
 
3.0%
S 90
 
1.1%
Other values (9) 603
7.0%

LandSlope
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size95.6 KiB
Gtl
1366 
Mod
 
64
Sev
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4317
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGtl
2nd rowGtl
3rd rowGtl
4th rowGtl
5th rowGtl

Common Values

ValueCountFrequency (%)
Gtl 1366
94.9%
Mod 64
 
4.4%
Sev 9
 
0.6%

Length

2024-09-29T16:16:33.948516image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:34.143153image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
gtl 1366
94.9%
mod 64
 
4.4%
sev 9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
G 1366
31.6%
t 1366
31.6%
l 1366
31.6%
M 64
 
1.5%
o 64
 
1.5%
d 64
 
1.5%
S 9
 
0.2%
e 9
 
0.2%
v 9
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 1366
31.6%
t 1366
31.6%
l 1366
31.6%
M 64
 
1.5%
o 64
 
1.5%
d 64
 
1.5%
S 9
 
0.2%
e 9
 
0.2%
v 9
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 1366
31.6%
t 1366
31.6%
l 1366
31.6%
M 64
 
1.5%
o 64
 
1.5%
d 64
 
1.5%
S 9
 
0.2%
e 9
 
0.2%
v 9
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4317
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 1366
31.6%
t 1366
31.6%
l 1366
31.6%
M 64
 
1.5%
o 64
 
1.5%
d 64
 
1.5%
S 9
 
0.2%
e 9
 
0.2%
v 9
 
0.2%

Neighborhood
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size100.5 KiB
NAmes
220 
CollgCr
149 
OldTown
113 
Edwards
97 
Somerst
86 
Other values (20)
774 

Length

Max length7
Median length7
Mean length6.4982627
Min length5

Characters and Unicode

Total characters9351
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCollgCr
2nd rowVeenker
3rd rowCollgCr
4th rowCrawfor
5th rowNoRidge

Common Values

ValueCountFrequency (%)
NAmes 220
15.3%
CollgCr 149
 
10.4%
OldTown 113
 
7.9%
Edwards 97
 
6.7%
Somerst 86
 
6.0%
Gilbert 78
 
5.4%
NridgHt 77
 
5.4%
Sawyer 73
 
5.1%
NWAmes 71
 
4.9%
SawyerW 59
 
4.1%
Other values (15) 416
28.9%

Length

2024-09-29T16:16:34.352742image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
names 220
15.3%
collgcr 149
 
10.4%
oldtown 113
 
7.9%
edwards 97
 
6.7%
somerst 86
 
6.0%
gilbert 78
 
5.4%
nridght 77
 
5.4%
sawyer 73
 
5.1%
nwames 71
 
4.9%
sawyerw 59
 
4.1%
Other values (15) 416
28.9%

Most occurring characters

ValueCountFrequency (%)
r 917
 
9.8%
e 890
 
9.5%
l 616
 
6.6%
d 499
 
5.3%
o 480
 
5.1%
s 476
 
5.1%
m 430
 
4.6%
N 417
 
4.5%
w 409
 
4.4%
C 400
 
4.3%
Other values (28) 3817
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9351
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 917
 
9.8%
e 890
 
9.5%
l 616
 
6.6%
d 499
 
5.3%
o 480
 
5.1%
s 476
 
5.1%
m 430
 
4.6%
N 417
 
4.5%
w 409
 
4.4%
C 400
 
4.3%
Other values (28) 3817
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9351
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 917
 
9.8%
e 890
 
9.5%
l 616
 
6.6%
d 499
 
5.3%
o 480
 
5.1%
s 476
 
5.1%
m 430
 
4.6%
N 417
 
4.5%
w 409
 
4.4%
C 400
 
4.3%
Other values (28) 3817
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9351
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 917
 
9.8%
e 890
 
9.5%
l 616
 
6.6%
d 499
 
5.3%
o 480
 
5.1%
s 476
 
5.1%
m 430
 
4.6%
N 417
 
4.5%
w 409
 
4.4%
C 400
 
4.3%
Other values (28) 3817
40.8%

Condition1
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size97.1 KiB
Norm
1245 
Feedr
 
79
Artery
 
46
RRAn
 
25
PosN
 
19
Other values (4)
 
25

Length

Max length6
Median length4
Mean length4.1188325
Min length4

Characters and Unicode

Total characters5927
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorm
2nd rowFeedr
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm 1245
86.5%
Feedr 79
 
5.5%
Artery 46
 
3.2%
RRAn 25
 
1.7%
PosN 19
 
1.3%
RRAe 11
 
0.8%
PosA 7
 
0.5%
RRNn 5
 
0.3%
RRNe 2
 
0.1%

Length

2024-09-29T16:16:34.654333image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:35.001385image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
norm 1245
86.5%
feedr 79
 
5.5%
artery 46
 
3.2%
rran 25
 
1.7%
posn 19
 
1.3%
rrae 11
 
0.8%
posa 7
 
0.5%
rrnn 5
 
0.3%
rrne 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 1416
23.9%
N 1271
21.4%
o 1271
21.4%
m 1245
21.0%
e 217
 
3.7%
A 89
 
1.5%
R 86
 
1.5%
F 79
 
1.3%
d 79
 
1.3%
t 46
 
0.8%
Other values (4) 128
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5927
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1416
23.9%
N 1271
21.4%
o 1271
21.4%
m 1245
21.0%
e 217
 
3.7%
A 89
 
1.5%
R 86
 
1.5%
F 79
 
1.3%
d 79
 
1.3%
t 46
 
0.8%
Other values (4) 128
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5927
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1416
23.9%
N 1271
21.4%
o 1271
21.4%
m 1245
21.0%
e 217
 
3.7%
A 89
 
1.5%
R 86
 
1.5%
F 79
 
1.3%
d 79
 
1.3%
t 46
 
0.8%
Other values (4) 128
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5927
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1416
23.9%
N 1271
21.4%
o 1271
21.4%
m 1245
21.0%
e 217
 
3.7%
A 89
 
1.5%
R 86
 
1.5%
F 79
 
1.3%
d 79
 
1.3%
t 46
 
0.8%
Other values (4) 128
 
2.2%

Condition2
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size97.0 KiB
Norm
1425 
Feedr
 
6
Artery
 
2
RRNn
 
2
PosN
 
2
Other values (2)
 
2

Length

Max length6
Median length4
Mean length4.0069493
Min length4

Characters and Unicode

Total characters5766
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowNorm
2nd rowNorm
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm 1425
99.0%
Feedr 6
 
0.4%
Artery 2
 
0.1%
RRNn 2
 
0.1%
PosN 2
 
0.1%
PosA 1
 
0.1%
RRAn 1
 
0.1%

Length

2024-09-29T16:16:35.309956image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:35.623556image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
norm 1425
99.0%
feedr 6
 
0.4%
artery 2
 
0.1%
rrnn 2
 
0.1%
posn 2
 
0.1%
posa 1
 
0.1%
rran 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 1435
24.9%
N 1429
24.8%
o 1428
24.8%
m 1425
24.7%
e 14
 
0.2%
F 6
 
0.1%
d 6
 
0.1%
R 6
 
0.1%
A 4
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1435
24.9%
N 1429
24.8%
o 1428
24.8%
m 1425
24.7%
e 14
 
0.2%
F 6
 
0.1%
d 6
 
0.1%
R 6
 
0.1%
A 4
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1435
24.9%
N 1429
24.8%
o 1428
24.8%
m 1425
24.7%
e 14
 
0.2%
F 6
 
0.1%
d 6
 
0.1%
R 6
 
0.1%
A 4
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1435
24.9%
N 1429
24.8%
o 1428
24.8%
m 1425
24.7%
e 14
 
0.2%
F 6
 
0.1%
d 6
 
0.1%
R 6
 
0.1%
A 4
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

BldgType
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size97.4 KiB
1Fam
1203 
TwnhsE
 
114
Duplex
 
50
Twnhs
 
43
2fmCon
 
29

Length

Max length6
Median length4
Mean length4.2981237
Min length4

Characters and Unicode

Total characters6185
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1Fam
2nd row1Fam
3rd row1Fam
4th row1Fam
5th row1Fam

Common Values

ValueCountFrequency (%)
1Fam 1203
83.6%
TwnhsE 114
 
7.9%
Duplex 50
 
3.5%
Twnhs 43
 
3.0%
2fmCon 29
 
2.0%

Length

2024-09-29T16:16:35.924824image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:36.157364image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1fam 1203
83.6%
twnhse 114
 
7.9%
duplex 50
 
3.5%
twnhs 43
 
3.0%
2fmcon 29
 
2.0%

Most occurring characters

ValueCountFrequency (%)
m 1232
19.9%
1 1203
19.5%
a 1203
19.5%
F 1203
19.5%
n 186
 
3.0%
T 157
 
2.5%
w 157
 
2.5%
h 157
 
2.5%
s 157
 
2.5%
E 114
 
1.8%
Other values (10) 416
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6185
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 1232
19.9%
1 1203
19.5%
a 1203
19.5%
F 1203
19.5%
n 186
 
3.0%
T 157
 
2.5%
w 157
 
2.5%
h 157
 
2.5%
s 157
 
2.5%
E 114
 
1.8%
Other values (10) 416
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6185
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 1232
19.9%
1 1203
19.5%
a 1203
19.5%
F 1203
19.5%
n 186
 
3.0%
T 157
 
2.5%
w 157
 
2.5%
h 157
 
2.5%
s 157
 
2.5%
E 114
 
1.8%
Other values (10) 416
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6185
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 1232
19.9%
1 1203
19.5%
a 1203
19.5%
F 1203
19.5%
n 186
 
3.0%
T 157
 
2.5%
w 157
 
2.5%
h 157
 
2.5%
s 157
 
2.5%
E 114
 
1.8%
Other values (10) 416
 
6.7%

HouseStyle
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size99.7 KiB
1Story
717 
2Story
439 
1.5Fin
150 
SLvl
 
63
SFoyer
 
37
Other values (3)
 
33

Length

Max length6
Median length6
Mean length5.9124392
Min length4

Characters and Unicode

Total characters8508
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2Story
2nd row1Story
3rd row2Story
4th row2Story
5th row2Story

Common Values

ValueCountFrequency (%)
1Story 717
49.8%
2Story 439
30.5%
1.5Fin 150
 
10.4%
SLvl 63
 
4.4%
SFoyer 37
 
2.6%
1.5Unf 14
 
1.0%
2.5Unf 11
 
0.8%
2.5Fin 8
 
0.6%

Length

2024-09-29T16:16:36.402978image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:36.731095image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1story 717
49.8%
2story 439
30.5%
1.5fin 150
 
10.4%
slvl 63
 
4.4%
sfoyer 37
 
2.6%
1.5unf 14
 
1.0%
2.5unf 11
 
0.8%
2.5fin 8
 
0.6%

Most occurring characters

ValueCountFrequency (%)
S 1256
14.8%
o 1193
14.0%
r 1193
14.0%
y 1193
14.0%
t 1156
13.6%
1 881
10.4%
2 458
 
5.4%
F 195
 
2.3%
5 183
 
2.2%
. 183
 
2.2%
Other values (8) 617
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8508
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1256
14.8%
o 1193
14.0%
r 1193
14.0%
y 1193
14.0%
t 1156
13.6%
1 881
10.4%
2 458
 
5.4%
F 195
 
2.3%
5 183
 
2.2%
. 183
 
2.2%
Other values (8) 617
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8508
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1256
14.8%
o 1193
14.0%
r 1193
14.0%
y 1193
14.0%
t 1156
13.6%
1 881
10.4%
2 458
 
5.4%
F 195
 
2.3%
5 183
 
2.2%
. 183
 
2.2%
Other values (8) 617
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8508
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1256
14.8%
o 1193
14.0%
r 1193
14.0%
y 1193
14.0%
t 1156
13.6%
1 881
10.4%
2 458
 
5.4%
F 195
 
2.3%
5 183
 
2.2%
. 183
 
2.2%
Other values (8) 617
7.3%

OverallQual
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
Average
392 
Above Average
369 
Good
313 
Very Good
166 
Below Average
115 
Other values (5)
84 

Length

Max length14
Median length13
Mean length8.6886727
Min length4

Characters and Unicode

Total characters12503
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowAbove Average
3rd rowGood
4th rowGood
5th rowVery Good

Common Values

ValueCountFrequency (%)
Average 392
27.2%
Above Average 369
25.6%
Good 313
21.8%
Very Good 166
11.5%
Below Average 115
 
8.0%
Excellent 43
 
3.0%
Fair 20
 
1.4%
Very Excellent 16
 
1.1%
Poor 3
 
0.2%
Very Poor 2
 
0.1%

Length

2024-09-29T16:16:36.985464image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:37.236008image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
average 876
41.6%
good 479
22.7%
above 369
17.5%
very 184
 
8.7%
below 115
 
5.5%
excellent 59
 
2.8%
fair 20
 
0.9%
poor 5
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 2538
20.3%
o 1452
11.6%
A 1245
10.0%
v 1245
10.0%
r 1085
8.7%
a 896
 
7.2%
g 876
 
7.0%
668
 
5.3%
G 479
 
3.8%
d 479
 
3.8%
Other values (14) 1540
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12503
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2538
20.3%
o 1452
11.6%
A 1245
10.0%
v 1245
10.0%
r 1085
8.7%
a 896
 
7.2%
g 876
 
7.0%
668
 
5.3%
G 479
 
3.8%
d 479
 
3.8%
Other values (14) 1540
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12503
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2538
20.3%
o 1452
11.6%
A 1245
10.0%
v 1245
10.0%
r 1085
8.7%
a 896
 
7.2%
g 876
 
7.0%
668
 
5.3%
G 479
 
3.8%
d 479
 
3.8%
Other values (14) 1540
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12503
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2538
20.3%
o 1452
11.6%
A 1245
10.0%
v 1245
10.0%
r 1085
8.7%
a 896
 
7.2%
g 876
 
7.0%
668
 
5.3%
G 479
 
3.8%
d 479
 
3.8%
Other values (14) 1540
12.3%

OverallCond
Categorical

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size102.5 KiB
Average
811 
Above Average
249 
Good
200 
Very Good
 
71
Below Average
 
56
Other values (4)
 
52

Length

Max length13
Median length7
Mean length7.9214732
Min length4

Characters and Unicode

Total characters11399
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAverage
2nd rowVery Good
3rd rowAverage
4th rowAverage
5th rowAverage

Common Values

ValueCountFrequency (%)
Average 811
56.4%
Above Average 249
 
17.3%
Good 200
 
13.9%
Very Good 71
 
4.9%
Below Average 56
 
3.9%
Fair 25
 
1.7%
Excellent 21
 
1.5%
Poor 5
 
0.3%
Very Poor 1
 
0.1%

Length

2024-09-29T16:16:37.519360image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:37.784288image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
average 1116
61.5%
good 271
 
14.9%
above 249
 
13.7%
very 72
 
4.0%
below 56
 
3.1%
fair 25
 
1.4%
excellent 21
 
1.2%
poor 6
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 2651
23.3%
A 1365
12.0%
v 1365
12.0%
r 1219
10.7%
a 1141
10.0%
g 1116
9.8%
o 859
 
7.5%
377
 
3.3%
G 271
 
2.4%
d 271
 
2.4%
Other values (14) 764
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11399
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2651
23.3%
A 1365
12.0%
v 1365
12.0%
r 1219
10.7%
a 1141
10.0%
g 1116
9.8%
o 859
 
7.5%
377
 
3.3%
G 271
 
2.4%
d 271
 
2.4%
Other values (14) 764
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11399
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2651
23.3%
A 1365
12.0%
v 1365
12.0%
r 1219
10.7%
a 1141
10.0%
g 1116
9.8%
o 859
 
7.5%
377
 
3.3%
G 271
 
2.4%
d 271
 
2.4%
Other values (14) 764
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11399
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2651
23.3%
A 1365
12.0%
v 1365
12.0%
r 1219
10.7%
a 1141
10.0%
g 1116
9.8%
o 859
 
7.5%
377
 
3.3%
G 271
 
2.4%
d 271
 
2.4%
Other values (14) 764
 
6.7%

RoofStyle
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size97.8 KiB
Gable
1130 
Hip
279 
Gambrel
 
11
Flat
 
11
Mansard
 
7

Length

Max length7
Median length5
Mean length4.628909
Min length3

Characters and Unicode

Total characters6661
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowGable
2nd rowGable
3rd rowGable
4th rowGable
5th rowGable

Common Values

ValueCountFrequency (%)
Gable 1130
78.5%
Hip 279
 
19.4%
Gambrel 11
 
0.8%
Flat 11
 
0.8%
Mansard 7
 
0.5%
Shed 1
 
0.1%

Length

2024-09-29T16:16:38.043787image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:38.342980image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
gable 1130
78.5%
hip 279
 
19.4%
gambrel 11
 
0.8%
flat 11
 
0.8%
mansard 7
 
0.5%
shed 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 1166
17.5%
l 1152
17.3%
e 1142
17.1%
G 1141
17.1%
b 1141
17.1%
H 279
 
4.2%
i 279
 
4.2%
p 279
 
4.2%
r 18
 
0.3%
m 11
 
0.2%
Other values (8) 53
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1166
17.5%
l 1152
17.3%
e 1142
17.1%
G 1141
17.1%
b 1141
17.1%
H 279
 
4.2%
i 279
 
4.2%
p 279
 
4.2%
r 18
 
0.3%
m 11
 
0.2%
Other values (8) 53
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1166
17.5%
l 1152
17.3%
e 1142
17.1%
G 1141
17.1%
b 1141
17.1%
H 279
 
4.2%
i 279
 
4.2%
p 279
 
4.2%
r 18
 
0.3%
m 11
 
0.2%
Other values (8) 53
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1166
17.5%
l 1152
17.3%
e 1142
17.1%
G 1141
17.1%
b 1141
17.1%
H 279
 
4.2%
i 279
 
4.2%
p 279
 
4.2%
r 18
 
0.3%
m 11
 
0.2%
Other values (8) 53
 
0.8%

RoofMatl
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size101.2 KiB
CompShg
1416 
Tar&Grv
 
9
WdShngl
 
6
WdShake
 
5
Metal
 
1
Other values (2)
 
2

Length

Max length7
Median length7
Mean length6.9965254
Min length4

Characters and Unicode

Total characters10068
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowCompShg
2nd rowCompShg
3rd rowCompShg
4th rowCompShg
5th rowCompShg

Common Values

ValueCountFrequency (%)
CompShg 1416
98.4%
Tar&Grv 9
 
0.6%
WdShngl 6
 
0.4%
WdShake 5
 
0.3%
Metal 1
 
0.1%
Membran 1
 
0.1%
Roll 1
 
0.1%

Length

2024-09-29T16:16:38.554731image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:38.722423image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
compshg 1416
98.4%
tar&grv 9
 
0.6%
wdshngl 6
 
0.4%
wdshake 5
 
0.3%
metal 1
 
0.1%
membran 1
 
0.1%
roll 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 1427
14.2%
h 1427
14.2%
g 1422
14.1%
m 1417
14.1%
o 1417
14.1%
C 1416
14.1%
p 1416
14.1%
r 19
 
0.2%
a 16
 
0.2%
W 11
 
0.1%
Other values (13) 80
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10068
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1427
14.2%
h 1427
14.2%
g 1422
14.1%
m 1417
14.1%
o 1417
14.1%
C 1416
14.1%
p 1416
14.1%
r 19
 
0.2%
a 16
 
0.2%
W 11
 
0.1%
Other values (13) 80
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10068
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1427
14.2%
h 1427
14.2%
g 1422
14.1%
m 1417
14.1%
o 1417
14.1%
C 1416
14.1%
p 1416
14.1%
r 19
 
0.2%
a 16
 
0.2%
W 11
 
0.1%
Other values (13) 80
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10068
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1427
14.2%
h 1427
14.2%
g 1422
14.1%
m 1417
14.1%
o 1417
14.1%
C 1416
14.1%
p 1416
14.1%
r 19
 
0.2%
a 16
 
0.2%
W 11
 
0.1%
Other values (13) 80
 
0.8%

Exterior1st
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size101.2 KiB
VinylSd
513 
HdBoard
220 
MetalSd
218 
Wd Sdng
202 
Plywood
101 
Other values (10)
185 

Length

Max length7
Median length7
Mean length6.980542
Min length5

Characters and Unicode

Total characters10045
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Sdng
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd 513
35.6%
HdBoard 220
15.3%
MetalSd 218
15.1%
Wd Sdng 202
 
14.0%
Plywood 101
 
7.0%
CemntBd 60
 
4.2%
BrkFace 49
 
3.4%
WdShing 26
 
1.8%
Stucco 23
 
1.6%
AsbShng 20
 
1.4%
Other values (5) 7
 
0.5%

Length

2024-09-29T16:16:38.902746image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd 513
31.3%
hdboard 220
13.4%
metalsd 218
13.3%
wd 202
 
12.3%
sdng 202
 
12.3%
plywood 101
 
6.2%
cemntbd 60
 
3.7%
brkface 49
 
3.0%
wdshing 26
 
1.6%
stucco 23
 
1.4%
Other values (6) 27
 
1.6%

Most occurring characters

ValueCountFrequency (%)
d 1762
17.5%
S 1006
 
10.0%
l 833
 
8.3%
n 824
 
8.2%
y 614
 
6.1%
i 539
 
5.4%
V 513
 
5.1%
a 487
 
4.8%
o 450
 
4.5%
B 332
 
3.3%
Other values (22) 2685
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10045
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1762
17.5%
S 1006
 
10.0%
l 833
 
8.3%
n 824
 
8.2%
y 614
 
6.1%
i 539
 
5.4%
V 513
 
5.1%
a 487
 
4.8%
o 450
 
4.5%
B 332
 
3.3%
Other values (22) 2685
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10045
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1762
17.5%
S 1006
 
10.0%
l 833
 
8.3%
n 824
 
8.2%
y 614
 
6.1%
i 539
 
5.4%
V 513
 
5.1%
a 487
 
4.8%
o 450
 
4.5%
B 332
 
3.3%
Other values (22) 2685
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10045
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1762
17.5%
S 1006
 
10.0%
l 833
 
8.3%
n 824
 
8.2%
y 614
 
6.1%
i 539
 
5.4%
V 513
 
5.1%
a 487
 
4.8%
o 450
 
4.5%
B 332
 
3.3%
Other values (22) 2685
26.7%

Exterior2nd
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size101.1 KiB
VinylSd
503 
MetalSd
212 
HdBoard
204 
Wd Sdng
195 
Plywood
135 
Other values (11)
190 

Length

Max length7
Median length7
Mean length6.9742877
Min length5

Characters and Unicode

Total characters10036
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Shng
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd 503
35.0%
MetalSd 212
14.7%
HdBoard 204
14.2%
Wd Sdng 195
 
13.6%
Plywood 135
 
9.4%
CmentBd 59
 
4.1%
Wd Shng 37
 
2.6%
BrkFace 24
 
1.7%
Stucco 24
 
1.7%
AsbShng 20
 
1.4%
Other values (6) 26
 
1.8%

Length

2024-09-29T16:16:39.104460image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd 503
30.0%
wd 232
13.8%
metalsd 212
12.6%
hdboard 204
12.2%
sdng 195
 
11.6%
plywood 135
 
8.0%
cmentbd 59
 
3.5%
shng 37
 
2.2%
stucco 24
 
1.4%
brkface 24
 
1.4%
Other values (8) 53
 
3.2%

Most occurring characters

ValueCountFrequency (%)
d 1744
17.4%
S 1008
 
10.0%
l 851
 
8.5%
n 829
 
8.3%
y 638
 
6.4%
o 504
 
5.0%
V 503
 
5.0%
i 503
 
5.0%
a 440
 
4.4%
t 310
 
3.1%
Other values (23) 2706
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10036
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1744
17.4%
S 1008
 
10.0%
l 851
 
8.5%
n 829
 
8.3%
y 638
 
6.4%
o 504
 
5.0%
V 503
 
5.0%
i 503
 
5.0%
a 440
 
4.4%
t 310
 
3.1%
Other values (23) 2706
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10036
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1744
17.4%
S 1008
 
10.0%
l 851
 
8.5%
n 829
 
8.3%
y 638
 
6.4%
o 504
 
5.0%
V 503
 
5.0%
i 503
 
5.0%
a 440
 
4.4%
t 310
 
3.1%
Other values (23) 2706
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10036
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1744
17.4%
S 1008
 
10.0%
l 851
 
8.5%
n 829
 
8.3%
y 638
 
6.4%
o 504
 
5.0%
V 503
 
5.0%
i 503
 
5.0%
a 440
 
4.4%
t 310
 
3.1%
Other values (23) 2706
27.0%

MasVnrType
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size96.7 KiB
NA
858 
BrkFace
442 
Stone
126 
BrkCmn
 
13

Length

Max length7
Median length2
Mean length3.8346074
Min length2

Characters and Unicode

Total characters5518
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrkFace
2nd rowNA
3rd rowBrkFace
4th rowNA
5th rowBrkFace

Common Values

ValueCountFrequency (%)
NA 858
59.6%
BrkFace 442
30.7%
Stone 126
 
8.8%
BrkCmn 13
 
0.9%

Length

2024-09-29T16:16:39.247040image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:39.376826image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
na 858
59.6%
brkface 442
30.7%
stone 126
 
8.8%
brkcmn 13
 
0.9%

Most occurring characters

ValueCountFrequency (%)
N 858
15.5%
A 858
15.5%
e 568
10.3%
B 455
8.2%
r 455
8.2%
k 455
8.2%
F 442
8.0%
a 442
8.0%
c 442
8.0%
n 139
 
2.5%
Other values (5) 404
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5518
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 858
15.5%
A 858
15.5%
e 568
10.3%
B 455
8.2%
r 455
8.2%
k 455
8.2%
F 442
8.0%
a 442
8.0%
c 442
8.0%
n 139
 
2.5%
Other values (5) 404
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5518
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 858
15.5%
A 858
15.5%
e 568
10.3%
B 455
8.2%
r 455
8.2%
k 455
8.2%
F 442
8.0%
a 442
8.0%
c 442
8.0%
n 139
 
2.5%
Other values (5) 404
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5518
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 858
15.5%
A 858
15.5%
e 568
10.3%
B 455
8.2%
r 455
8.2%
k 455
8.2%
F 442
8.0%
a 442
8.0%
c 442
8.0%
n 139
 
2.5%
Other values (5) 404
7.3%

ExterQual
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size94.2 KiB
TA
894 
Gd
482 
Ex
 
50
Fa
 
13

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2878
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA 894
62.1%
Gd 482
33.5%
Ex 50
 
3.5%
Fa 13
 
0.9%

Length

2024-09-29T16:16:39.558522image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:39.757449image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
ta 894
62.1%
gd 482
33.5%
ex 50
 
3.5%
fa 13
 
0.9%

Most occurring characters

ValueCountFrequency (%)
T 894
31.1%
A 894
31.1%
G 482
16.7%
d 482
16.7%
E 50
 
1.7%
x 50
 
1.7%
F 13
 
0.5%
a 13
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 894
31.1%
A 894
31.1%
G 482
16.7%
d 482
16.7%
E 50
 
1.7%
x 50
 
1.7%
F 13
 
0.5%
a 13
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 894
31.1%
A 894
31.1%
G 482
16.7%
d 482
16.7%
E 50
 
1.7%
x 50
 
1.7%
F 13
 
0.5%
a 13
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 894
31.1%
A 894
31.1%
G 482
16.7%
d 482
16.7%
E 50
 
1.7%
x 50
 
1.7%
F 13
 
0.5%
a 13
 
0.5%

ExterCond
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size94.2 KiB
TA
1267 
Gd
141 
Fa
 
27
Ex
 
3
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2878
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1267
88.0%
Gd 141
 
9.8%
Fa 27
 
1.9%
Ex 3
 
0.2%
Po 1
 
0.1%

Length

2024-09-29T16:16:39.978761image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:40.139126image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
ta 1267
88.0%
gd 141
 
9.8%
fa 27
 
1.9%
ex 3
 
0.2%
po 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1267
44.0%
A 1267
44.0%
G 141
 
4.9%
d 141
 
4.9%
F 27
 
0.9%
a 27
 
0.9%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1267
44.0%
A 1267
44.0%
G 141
 
4.9%
d 141
 
4.9%
F 27
 
0.9%
a 27
 
0.9%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1267
44.0%
A 1267
44.0%
G 141
 
4.9%
d 141
 
4.9%
F 27
 
0.9%
a 27
 
0.9%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1267
44.0%
A 1267
44.0%
G 141
 
4.9%
d 141
 
4.9%
F 27
 
0.9%
a 27
 
0.9%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Foundation
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size99.1 KiB
PConc
642 
CBlock
620 
BrkTil
146 
Slab
 
23
Stone
 
5

Length

Max length6
Median length6
Mean length5.514246
Min length4

Characters and Unicode

Total characters7935
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPConc
2nd rowCBlock
3rd rowPConc
4th rowBrkTil
5th rowPConc

Common Values

ValueCountFrequency (%)
PConc 642
44.6%
CBlock 620
43.1%
BrkTil 146
 
10.1%
Slab 23
 
1.6%
Stone 5
 
0.3%
Wood 3
 
0.2%

Length

2024-09-29T16:16:40.334549image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:40.525118image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
pconc 642
44.6%
cblock 620
43.1%
brktil 146
 
10.1%
slab 23
 
1.6%
stone 5
 
0.3%
wood 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o 1273
16.0%
C 1262
15.9%
c 1262
15.9%
l 789
9.9%
B 766
9.7%
k 766
9.7%
n 647
8.2%
P 642
8.1%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 236
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7935
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1273
16.0%
C 1262
15.9%
c 1262
15.9%
l 789
9.9%
B 766
9.7%
k 766
9.7%
n 647
8.2%
P 642
8.1%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 236
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7935
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1273
16.0%
C 1262
15.9%
c 1262
15.9%
l 789
9.9%
B 766
9.7%
k 766
9.7%
n 647
8.2%
P 642
8.1%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 236
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7935
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1273
16.0%
C 1262
15.9%
c 1262
15.9%
l 789
9.9%
B 766
9.7%
k 766
9.7%
n 647
8.2%
P 642
8.1%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 236
 
3.0%

BsmtQual
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size94.2 KiB
TA
641 
Gd
608 
Ex
119 
NA
 
36
Fa
 
35

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2878
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowGd
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA 641
44.5%
Gd 608
42.3%
Ex 119
 
8.3%
NA 36
 
2.5%
Fa 35
 
2.4%

Length

2024-09-29T16:16:40.706381image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:40.965254image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
ta 641
44.5%
gd 608
42.3%
ex 119
 
8.3%
na 36
 
2.5%
fa 35
 
2.4%

Most occurring characters

ValueCountFrequency (%)
A 677
23.5%
T 641
22.3%
G 608
21.1%
d 608
21.1%
E 119
 
4.1%
x 119
 
4.1%
N 36
 
1.3%
F 35
 
1.2%
a 35
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 677
23.5%
T 641
22.3%
G 608
21.1%
d 608
21.1%
E 119
 
4.1%
x 119
 
4.1%
N 36
 
1.3%
F 35
 
1.2%
a 35
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 677
23.5%
T 641
22.3%
G 608
21.1%
d 608
21.1%
E 119
 
4.1%
x 119
 
4.1%
N 36
 
1.3%
F 35
 
1.2%
a 35
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 677
23.5%
T 641
22.3%
G 608
21.1%
d 608
21.1%
E 119
 
4.1%
x 119
 
4.1%
N 36
 
1.3%
F 35
 
1.2%
a 35
 
1.2%

BsmtCond
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size94.2 KiB
TA
1293 
Gd
 
63
Fa
 
45
NA
 
36
Po
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2878
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowGd
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1293
89.9%
Gd 63
 
4.4%
Fa 45
 
3.1%
NA 36
 
2.5%
Po 2
 
0.1%

Length

2024-09-29T16:16:41.254092image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:41.476661image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
ta 1293
89.9%
gd 63
 
4.4%
fa 45
 
3.1%
na 36
 
2.5%
po 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 1329
46.2%
T 1293
44.9%
G 63
 
2.2%
d 63
 
2.2%
F 45
 
1.6%
a 45
 
1.6%
N 36
 
1.3%
P 2
 
0.1%
o 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1329
46.2%
T 1293
44.9%
G 63
 
2.2%
d 63
 
2.2%
F 45
 
1.6%
a 45
 
1.6%
N 36
 
1.3%
P 2
 
0.1%
o 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1329
46.2%
T 1293
44.9%
G 63
 
2.2%
d 63
 
2.2%
F 45
 
1.6%
a 45
 
1.6%
N 36
 
1.3%
P 2
 
0.1%
o 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1329
46.2%
T 1293
44.9%
G 63
 
2.2%
d 63
 
2.2%
F 45
 
1.6%
a 45
 
1.6%
N 36
 
1.3%
P 2
 
0.1%
o 2
 
0.1%

BsmtExposure
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size94.2 KiB
No
946 
Av
217 
Gd
127 
Mn
112 
NA
 
37

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2878
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowGd
3rd rowMn
4th rowNo
5th rowAv

Common Values

ValueCountFrequency (%)
No 946
65.7%
Av 217
 
15.1%
Gd 127
 
8.8%
Mn 112
 
7.8%
NA 37
 
2.6%

Length

2024-09-29T16:16:41.792373image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:42.061812image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
no 946
65.7%
av 217
 
15.1%
gd 127
 
8.8%
mn 112
 
7.8%
na 37
 
2.6%

Most occurring characters

ValueCountFrequency (%)
N 983
34.2%
o 946
32.9%
A 254
 
8.8%
v 217
 
7.5%
G 127
 
4.4%
d 127
 
4.4%
M 112
 
3.9%
n 112
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 983
34.2%
o 946
32.9%
A 254
 
8.8%
v 217
 
7.5%
G 127
 
4.4%
d 127
 
4.4%
M 112
 
3.9%
n 112
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 983
34.2%
o 946
32.9%
A 254
 
8.8%
v 217
 
7.5%
G 127
 
4.4%
d 127
 
4.4%
M 112
 
3.9%
n 112
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 983
34.2%
o 946
32.9%
A 254
 
8.8%
v 217
 
7.5%
G 127
 
4.4%
d 127
 
4.4%
M 112
 
3.9%
n 112
 
3.9%

BsmtFinType1
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size95.5 KiB
Unf
427 
GLQ
409 
ALQ
216 
BLQ
145 
Rec
132 
Other values (2)
110 

Length

Max length3
Median length3
Mean length2.9749826
Min length2

Characters and Unicode

Total characters4281
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGLQ
2nd rowALQ
3rd rowGLQ
4th rowALQ
5th rowGLQ

Common Values

ValueCountFrequency (%)
Unf 427
29.7%
GLQ 409
28.4%
ALQ 216
15.0%
BLQ 145
 
10.1%
Rec 132
 
9.2%
LwQ 74
 
5.1%
NA 36
 
2.5%

Length

2024-09-29T16:16:42.338862image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:42.586446image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
unf 427
29.7%
glq 409
28.4%
alq 216
15.0%
blq 145
 
10.1%
rec 132
 
9.2%
lwq 74
 
5.1%
na 36
 
2.5%

Most occurring characters

ValueCountFrequency (%)
L 844
19.7%
Q 844
19.7%
U 427
10.0%
n 427
10.0%
f 427
10.0%
G 409
9.6%
A 252
 
5.9%
B 145
 
3.4%
R 132
 
3.1%
e 132
 
3.1%
Other values (3) 242
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4281
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 844
19.7%
Q 844
19.7%
U 427
10.0%
n 427
10.0%
f 427
10.0%
G 409
9.6%
A 252
 
5.9%
B 145
 
3.4%
R 132
 
3.1%
e 132
 
3.1%
Other values (3) 242
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4281
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 844
19.7%
Q 844
19.7%
U 427
10.0%
n 427
10.0%
f 427
10.0%
G 409
9.6%
A 252
 
5.9%
B 145
 
3.4%
R 132
 
3.1%
e 132
 
3.1%
Other values (3) 242
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4281
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 844
19.7%
Q 844
19.7%
U 427
10.0%
n 427
10.0%
f 427
10.0%
G 409
9.6%
A 252
 
5.9%
B 145
 
3.4%
R 132
 
3.1%
e 132
 
3.1%
Other values (3) 242
 
5.7%

BsmtFinType2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size95.5 KiB
Unf
1243 
Rec
 
51
LwQ
 
43
NA
 
37
BLQ
 
32
Other values (2)
 
33

Length

Max length3
Median length3
Mean length2.9742877
Min length2

Characters and Unicode

Total characters4280
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnf
2nd rowUnf
3rd rowUnf
4th rowUnf
5th rowUnf

Common Values

ValueCountFrequency (%)
Unf 1243
86.4%
Rec 51
 
3.5%
LwQ 43
 
3.0%
NA 37
 
2.6%
BLQ 32
 
2.2%
ALQ 19
 
1.3%
GLQ 14
 
1.0%

Length

2024-09-29T16:16:42.883584image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:43.130350image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
unf 1243
86.4%
rec 51
 
3.5%
lwq 43
 
3.0%
na 37
 
2.6%
blq 32
 
2.2%
alq 19
 
1.3%
glq 14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
U 1243
29.0%
n 1243
29.0%
f 1243
29.0%
L 108
 
2.5%
Q 108
 
2.5%
A 56
 
1.3%
R 51
 
1.2%
e 51
 
1.2%
c 51
 
1.2%
w 43
 
1.0%
Other values (3) 83
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4280
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 1243
29.0%
n 1243
29.0%
f 1243
29.0%
L 108
 
2.5%
Q 108
 
2.5%
A 56
 
1.3%
R 51
 
1.2%
e 51
 
1.2%
c 51
 
1.2%
w 43
 
1.0%
Other values (3) 83
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4280
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 1243
29.0%
n 1243
29.0%
f 1243
29.0%
L 108
 
2.5%
Q 108
 
2.5%
A 56
 
1.3%
R 51
 
1.2%
e 51
 
1.2%
c 51
 
1.2%
w 43
 
1.0%
Other values (3) 83
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4280
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 1243
29.0%
n 1243
29.0%
f 1243
29.0%
L 108
 
2.5%
Q 108
 
2.5%
A 56
 
1.3%
R 51
 
1.2%
e 51
 
1.2%
c 51
 
1.2%
w 43
 
1.0%
Other values (3) 83
 
1.9%

Heating
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size97.0 KiB
GasA
1408 
GasW
 
17
Grav
 
7
Wall
 
4
OthW
 
2

Length

Max length5
Median length4
Mean length4.0006949
Min length4

Characters and Unicode

Total characters5757
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowGasA
2nd rowGasA
3rd rowGasA
4th rowGasA
5th rowGasA

Common Values

ValueCountFrequency (%)
GasA 1408
97.8%
GasW 17
 
1.2%
Grav 7
 
0.5%
Wall 4
 
0.3%
OthW 2
 
0.1%
Floor 1
 
0.1%

Length

2024-09-29T16:16:43.374489image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:43.576430image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
gasa 1408
97.8%
gasw 17
 
1.2%
grav 7
 
0.5%
wall 4
 
0.3%
othw 2
 
0.1%
floor 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 1436
24.9%
G 1432
24.9%
s 1425
24.8%
A 1408
24.5%
W 23
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1436
24.9%
G 1432
24.9%
s 1425
24.8%
A 1408
24.5%
W 23
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1436
24.9%
G 1432
24.9%
s 1425
24.8%
A 1408
24.5%
W 23
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1436
24.9%
G 1432
24.9%
s 1425
24.8%
A 1408
24.5%
W 23
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

HeatingQC
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size94.2 KiB
Ex
731 
TA
421 
Gd
239 
Fa
 
47
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2878
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowEx
2nd rowEx
3rd rowEx
4th rowGd
5th rowEx

Common Values

ValueCountFrequency (%)
Ex 731
50.8%
TA 421
29.3%
Gd 239
 
16.6%
Fa 47
 
3.3%
Po 1
 
0.1%

Length

2024-09-29T16:16:43.811018image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:44.045425image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
ex 731
50.8%
ta 421
29.3%
gd 239
 
16.6%
fa 47
 
3.3%
po 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 731
25.4%
x 731
25.4%
T 421
14.6%
A 421
14.6%
G 239
 
8.3%
d 239
 
8.3%
F 47
 
1.6%
a 47
 
1.6%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 731
25.4%
x 731
25.4%
T 421
14.6%
A 421
14.6%
G 239
 
8.3%
d 239
 
8.3%
F 47
 
1.6%
a 47
 
1.6%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 731
25.4%
x 731
25.4%
T 421
14.6%
A 421
14.6%
G 239
 
8.3%
d 239
 
8.3%
F 47
 
1.6%
a 47
 
1.6%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 731
25.4%
x 731
25.4%
T 421
14.6%
A 421
14.6%
G 239
 
8.3%
d 239
 
8.3%
F 47
 
1.6%
a 47
 
1.6%
P 1
 
< 0.1%
o 1
 
< 0.1%

CentralAir
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
True
1345 
False
 
94
ValueCountFrequency (%)
True 1345
93.5%
False 94
 
6.5%
2024-09-29T16:16:44.326407image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Electrical
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size98.4 KiB
SBrkr
1313 
FuseA
 
94
FuseF
 
27
FuseP
 
3
Mix
 
1

Length

Max length5
Median length5
Mean length4.9965254
Min length2

Characters and Unicode

Total characters7190
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowSBrkr
2nd rowSBrkr
3rd rowSBrkr
4th rowSBrkr
5th rowSBrkr

Common Values

ValueCountFrequency (%)
SBrkr 1313
91.2%
FuseA 94
 
6.5%
FuseF 27
 
1.9%
FuseP 3
 
0.2%
Mix 1
 
0.1%
NA 1
 
0.1%

Length

2024-09-29T16:16:44.583067image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:44.895033image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
sbrkr 1313
91.2%
fusea 94
 
6.5%
fusef 27
 
1.9%
fusep 3
 
0.2%
mix 1
 
0.1%
na 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 2626
36.5%
S 1313
18.3%
B 1313
18.3%
k 1313
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 95
 
1.3%
P 3
 
< 0.1%
Other values (4) 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 2626
36.5%
S 1313
18.3%
B 1313
18.3%
k 1313
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 95
 
1.3%
P 3
 
< 0.1%
Other values (4) 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 2626
36.5%
S 1313
18.3%
B 1313
18.3%
k 1313
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 95
 
1.3%
P 3
 
< 0.1%
Other values (4) 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 2626
36.5%
S 1313
18.3%
B 1313
18.3%
k 1313
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 95
 
1.3%
P 3
 
< 0.1%
Other values (4) 4
 
0.1%

KitchenQual
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size94.2 KiB
TA
726 
Gd
577 
Ex
98 
Fa
 
38

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2878
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowGd
5th rowGd

Common Values

ValueCountFrequency (%)
TA 726
50.5%
Gd 577
40.1%
Ex 98
 
6.8%
Fa 38
 
2.6%

Length

2024-09-29T16:16:45.057438image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:45.254907image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
ta 726
50.5%
gd 577
40.1%
ex 98
 
6.8%
fa 38
 
2.6%

Most occurring characters

ValueCountFrequency (%)
T 726
25.2%
A 726
25.2%
G 577
20.0%
d 577
20.0%
E 98
 
3.4%
x 98
 
3.4%
F 38
 
1.3%
a 38
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 726
25.2%
A 726
25.2%
G 577
20.0%
d 577
20.0%
E 98
 
3.4%
x 98
 
3.4%
F 38
 
1.3%
a 38
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 726
25.2%
A 726
25.2%
G 577
20.0%
d 577
20.0%
E 98
 
3.4%
x 98
 
3.4%
F 38
 
1.3%
a 38
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 726
25.2%
A 726
25.2%
G 577
20.0%
d 577
20.0%
E 98
 
3.4%
x 98
 
3.4%
F 38
 
1.3%
a 38
 
1.3%

Functional
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size95.6 KiB
Typ
1341 
Min2
 
34
Min1
 
30
Maj1
 
14
Mod
 
14
Other values (2)
 
6

Length

Max length4
Median length3
Mean length3.0576789
Min length3

Characters and Unicode

Total characters4400
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTyp
2nd rowTyp
3rd rowTyp
4th rowTyp
5th rowTyp

Common Values

ValueCountFrequency (%)
Typ 1341
93.2%
Min2 34
 
2.4%
Min1 30
 
2.1%
Maj1 14
 
1.0%
Mod 14
 
1.0%
Maj2 5
 
0.3%
Sev 1
 
0.1%

Length

2024-09-29T16:16:45.452908image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:45.713912image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
typ 1341
93.2%
min2 34
 
2.4%
min1 30
 
2.1%
maj1 14
 
1.0%
mod 14
 
1.0%
maj2 5
 
0.3%
sev 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1341
30.5%
y 1341
30.5%
p 1341
30.5%
M 97
 
2.2%
i 64
 
1.5%
n 64
 
1.5%
1 44
 
1.0%
2 39
 
0.9%
a 19
 
0.4%
j 19
 
0.4%
Other values (5) 31
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1341
30.5%
y 1341
30.5%
p 1341
30.5%
M 97
 
2.2%
i 64
 
1.5%
n 64
 
1.5%
1 44
 
1.0%
2 39
 
0.9%
a 19
 
0.4%
j 19
 
0.4%
Other values (5) 31
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1341
30.5%
y 1341
30.5%
p 1341
30.5%
M 97
 
2.2%
i 64
 
1.5%
n 64
 
1.5%
1 44
 
1.0%
2 39
 
0.9%
a 19
 
0.4%
j 19
 
0.4%
Other values (5) 31
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1341
30.5%
y 1341
30.5%
p 1341
30.5%
M 97
 
2.2%
i 64
 
1.5%
n 64
 
1.5%
1 44
 
1.0%
2 39
 
0.9%
a 19
 
0.4%
j 19
 
0.4%
Other values (5) 31
 
0.7%

FireplaceQu
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size94.2 KiB
NA
686 
Gd
374 
TA
306 
Fa
 
32
Ex
 
23

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2878
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNA
2nd rowTA
3rd rowTA
4th rowGd
5th rowTA

Common Values

ValueCountFrequency (%)
NA 686
47.7%
Gd 374
26.0%
TA 306
21.3%
Fa 32
 
2.2%
Ex 23
 
1.6%
Po 18
 
1.3%

Length

2024-09-29T16:16:46.010700image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:46.280647image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
na 686
47.7%
gd 374
26.0%
ta 306
21.3%
fa 32
 
2.2%
ex 23
 
1.6%
po 18
 
1.3%

Most occurring characters

ValueCountFrequency (%)
A 992
34.5%
N 686
23.8%
G 374
 
13.0%
d 374
 
13.0%
T 306
 
10.6%
F 32
 
1.1%
a 32
 
1.1%
E 23
 
0.8%
x 23
 
0.8%
P 18
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 992
34.5%
N 686
23.8%
G 374
 
13.0%
d 374
 
13.0%
T 306
 
10.6%
F 32
 
1.1%
a 32
 
1.1%
E 23
 
0.8%
x 23
 
0.8%
P 18
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 992
34.5%
N 686
23.8%
G 374
 
13.0%
d 374
 
13.0%
T 306
 
10.6%
F 32
 
1.1%
a 32
 
1.1%
E 23
 
0.8%
x 23
 
0.8%
P 18
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 992
34.5%
N 686
23.8%
G 374
 
13.0%
d 374
 
13.0%
T 306
 
10.6%
F 32
 
1.1%
a 32
 
1.1%
E 23
 
0.8%
x 23
 
0.8%
P 18
 
0.6%

GarageType
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size99.6 KiB
Attchd
855 
Detchd
385 
BuiltIn
87 
NA
 
79
Basment
 
19
Other values (2)
 
14

Length

Max length7
Median length6
Mean length5.8596247
Min length2

Characters and Unicode

Total characters8432
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAttchd
2nd rowAttchd
3rd rowAttchd
4th rowDetchd
5th rowAttchd

Common Values

ValueCountFrequency (%)
Attchd 855
59.4%
Detchd 385
26.8%
BuiltIn 87
 
6.0%
NA 79
 
5.5%
Basment 19
 
1.3%
CarPort 8
 
0.6%
2Types 6
 
0.4%

Length

2024-09-29T16:16:46.560498image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:46.816392image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
attchd 855
59.4%
detchd 385
26.8%
builtin 87
 
6.0%
na 79
 
5.5%
basment 19
 
1.3%
carport 8
 
0.6%
2types 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
t 2209
26.2%
c 1240
14.7%
h 1240
14.7%
d 1240
14.7%
A 934
11.1%
e 410
 
4.9%
D 385
 
4.6%
n 106
 
1.3%
B 106
 
1.3%
u 87
 
1.0%
Other values (15) 475
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8432
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 2209
26.2%
c 1240
14.7%
h 1240
14.7%
d 1240
14.7%
A 934
11.1%
e 410
 
4.9%
D 385
 
4.6%
n 106
 
1.3%
B 106
 
1.3%
u 87
 
1.0%
Other values (15) 475
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8432
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 2209
26.2%
c 1240
14.7%
h 1240
14.7%
d 1240
14.7%
A 934
11.1%
e 410
 
4.9%
D 385
 
4.6%
n 106
 
1.3%
B 106
 
1.3%
u 87
 
1.0%
Other values (15) 475
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8432
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 2209
26.2%
c 1240
14.7%
h 1240
14.7%
d 1240
14.7%
A 934
11.1%
e 410
 
4.9%
D 385
 
4.6%
n 106
 
1.3%
B 106
 
1.3%
u 87
 
1.0%
Other values (15) 475
 
5.6%

GarageFinish
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size95.5 KiB
Unf
600 
RFn
413 
Fin
347 
NA
79 

Length

Max length3
Median length3
Mean length2.9451008
Min length2

Characters and Unicode

Total characters4238
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRFn
2nd rowRFn
3rd rowRFn
4th rowUnf
5th rowRFn

Common Values

ValueCountFrequency (%)
Unf 600
41.7%
RFn 413
28.7%
Fin 347
24.1%
NA 79
 
5.5%

Length

2024-09-29T16:16:47.154536image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:47.285074image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
unf 600
41.7%
rfn 413
28.7%
fin 347
24.1%
na 79
 
5.5%

Most occurring characters

ValueCountFrequency (%)
n 1360
32.1%
F 760
17.9%
U 600
14.2%
f 600
14.2%
R 413
 
9.7%
i 347
 
8.2%
N 79
 
1.9%
A 79
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4238
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1360
32.1%
F 760
17.9%
U 600
14.2%
f 600
14.2%
R 413
 
9.7%
i 347
 
8.2%
N 79
 
1.9%
A 79
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4238
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1360
32.1%
F 760
17.9%
U 600
14.2%
f 600
14.2%
R 413
 
9.7%
i 347
 
8.2%
N 79
 
1.9%
A 79
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4238
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1360
32.1%
F 760
17.9%
U 600
14.2%
f 600
14.2%
R 413
 
9.7%
i 347
 
8.2%
N 79
 
1.9%
A 79
 
1.9%

GarageQual
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size94.2 KiB
TA
1293 
NA
 
79
Fa
 
48
Gd
 
13
Ex
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2878
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1293
89.9%
NA 79
 
5.5%
Fa 48
 
3.3%
Gd 13
 
0.9%
Ex 3
 
0.2%
Po 3
 
0.2%

Length

2024-09-29T16:16:47.523748image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:47.708389image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
ta 1293
89.9%
na 79
 
5.5%
fa 48
 
3.3%
gd 13
 
0.9%
ex 3
 
0.2%
po 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A 1372
47.7%
T 1293
44.9%
N 79
 
2.7%
F 48
 
1.7%
a 48
 
1.7%
G 13
 
0.5%
d 13
 
0.5%
E 3
 
0.1%
x 3
 
0.1%
P 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1372
47.7%
T 1293
44.9%
N 79
 
2.7%
F 48
 
1.7%
a 48
 
1.7%
G 13
 
0.5%
d 13
 
0.5%
E 3
 
0.1%
x 3
 
0.1%
P 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1372
47.7%
T 1293
44.9%
N 79
 
2.7%
F 48
 
1.7%
a 48
 
1.7%
G 13
 
0.5%
d 13
 
0.5%
E 3
 
0.1%
x 3
 
0.1%
P 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1372
47.7%
T 1293
44.9%
N 79
 
2.7%
F 48
 
1.7%
a 48
 
1.7%
G 13
 
0.5%
d 13
 
0.5%
E 3
 
0.1%
x 3
 
0.1%
P 3
 
0.1%

GarageCond
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size94.2 KiB
TA
1308 
NA
 
79
Fa
 
35
Gd
 
8
Po
 
7

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2878
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1308
90.9%
NA 79
 
5.5%
Fa 35
 
2.4%
Gd 8
 
0.6%
Po 7
 
0.5%
Ex 2
 
0.1%

Length

2024-09-29T16:16:47.935380image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:48.208704image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
ta 1308
90.9%
na 79
 
5.5%
fa 35
 
2.4%
gd 8
 
0.6%
po 7
 
0.5%
ex 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 1387
48.2%
T 1308
45.4%
N 79
 
2.7%
F 35
 
1.2%
a 35
 
1.2%
G 8
 
0.3%
d 8
 
0.3%
P 7
 
0.2%
o 7
 
0.2%
E 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1387
48.2%
T 1308
45.4%
N 79
 
2.7%
F 35
 
1.2%
a 35
 
1.2%
G 8
 
0.3%
d 8
 
0.3%
P 7
 
0.2%
o 7
 
0.2%
E 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1387
48.2%
T 1308
45.4%
N 79
 
2.7%
F 35
 
1.2%
a 35
 
1.2%
G 8
 
0.3%
d 8
 
0.3%
P 7
 
0.2%
o 7
 
0.2%
E 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1387
48.2%
T 1308
45.4%
N 79
 
2.7%
F 35
 
1.2%
a 35
 
1.2%
G 8
 
0.3%
d 8
 
0.3%
P 7
 
0.2%
o 7
 
0.2%
E 2
 
0.1%

PavedDrive
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size92.7 KiB
Y
1321 
N
 
88
P
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1439
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 1321
91.8%
N 88
 
6.1%
P 30
 
2.1%

Length

2024-09-29T16:16:48.440361image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:48.722379image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
y 1321
91.8%
n 88
 
6.1%
p 30
 
2.1%

Most occurring characters

ValueCountFrequency (%)
Y 1321
91.8%
N 88
 
6.1%
P 30
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1439
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 1321
91.8%
N 88
 
6.1%
P 30
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1439
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 1321
91.8%
N 88
 
6.1%
P 30
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1439
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 1321
91.8%
N 88
 
6.1%
P 30
 
2.1%

SaleType
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size94.4 KiB
WD
1247 
New
 
121
COD
 
43
ConLD
 
9
ConLI
 
5
Other values (4)
 
14

Length

Max length5
Median length2
Mean length2.1598332
Min length2

Characters and Unicode

Total characters3108
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWD
2nd rowWD
3rd rowWD
4th rowWD
5th rowWD

Common Values

ValueCountFrequency (%)
WD 1247
86.7%
New 121
 
8.4%
COD 43
 
3.0%
ConLD 9
 
0.6%
ConLI 5
 
0.3%
ConLw 5
 
0.3%
CWD 4
 
0.3%
Oth 3
 
0.2%
Con 2
 
0.1%

Length

2024-09-29T16:16:48.941152image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:49.246096image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
wd 1247
86.7%
new 121
 
8.4%
cod 43
 
3.0%
conld 9
 
0.6%
conli 5
 
0.3%
conlw 5
 
0.3%
cwd 4
 
0.3%
oth 3
 
0.2%
con 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
D 1303
41.9%
W 1251
40.3%
w 126
 
4.1%
N 121
 
3.9%
e 121
 
3.9%
C 68
 
2.2%
O 46
 
1.5%
o 21
 
0.7%
n 21
 
0.7%
L 19
 
0.6%
Other values (3) 11
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3108
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 1303
41.9%
W 1251
40.3%
w 126
 
4.1%
N 121
 
3.9%
e 121
 
3.9%
C 68
 
2.2%
O 46
 
1.5%
o 21
 
0.7%
n 21
 
0.7%
L 19
 
0.6%
Other values (3) 11
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3108
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 1303
41.9%
W 1251
40.3%
w 126
 
4.1%
N 121
 
3.9%
e 121
 
3.9%
C 68
 
2.2%
O 46
 
1.5%
o 21
 
0.7%
n 21
 
0.7%
L 19
 
0.6%
Other values (3) 11
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3108
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 1303
41.9%
W 1251
40.3%
w 126
 
4.1%
N 121
 
3.9%
e 121
 
3.9%
C 68
 
2.2%
O 46
 
1.5%
o 21
 
0.7%
n 21
 
0.7%
L 19
 
0.6%
Other values (3) 11
 
0.4%

SaleCondition
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size100.0 KiB
Normal
1182 
Partial
124 
Abnorml
 
99
Family
 
20
Alloca
 
10

Length

Max length7
Median length6
Mean length6.1577484
Min length6

Characters and Unicode

Total characters8861
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowAbnorml
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 1182
82.1%
Partial 124
 
8.6%
Abnorml 99
 
6.9%
Family 20
 
1.4%
Alloca 10
 
0.7%
AdjLand 4
 
0.3%

Length

2024-09-29T16:16:49.486221image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T16:16:49.703344image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
normal 1182
82.1%
partial 124
 
8.6%
abnorml 99
 
6.9%
family 20
 
1.4%
alloca 10
 
0.7%
adjland 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a 1464
16.5%
l 1445
16.3%
r 1405
15.9%
m 1301
14.7%
o 1291
14.6%
N 1182
13.3%
i 144
 
1.6%
P 124
 
1.4%
t 124
 
1.4%
A 113
 
1.3%
Other values (8) 268
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8861
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1464
16.5%
l 1445
16.3%
r 1405
15.9%
m 1301
14.7%
o 1291
14.6%
N 1182
13.3%
i 144
 
1.6%
P 124
 
1.4%
t 124
 
1.4%
A 113
 
1.3%
Other values (8) 268
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8861
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1464
16.5%
l 1445
16.3%
r 1405
15.9%
m 1301
14.7%
o 1291
14.6%
N 1182
13.3%
i 144
 
1.6%
P 124
 
1.4%
t 124
 
1.4%
A 113
 
1.3%
Other values (8) 268
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8861
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1464
16.5%
l 1445
16.3%
r 1405
15.9%
m 1301
14.7%
o 1291
14.6%
N 1182
13.3%
i 144
 
1.6%
P 124
 
1.4%
t 124
 
1.4%
A 113
 
1.3%
Other values (8) 268
 
3.0%

Interactions

2024-09-29T16:16:08.636578image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:12:54.790643image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:12:59.979107image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:04.480182image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:09.533567image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:14.787909image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:19.631687image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:23.629099image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:27.831712image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:37.616638image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:50.634866image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:03.934745image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:15.133331image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:28.880076image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:40.529489image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:53.034703image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:04.679054image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:16.664431image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:30.101442image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:38.898605image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:43.230975image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:48.152609image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:53.265335image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:58.853305image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:04.768999image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:08.909730image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:12:54.986902image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:00.202817image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:04.643363image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:09.750072image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:15.065746image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:19.768806image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:23.823714image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:28.031736image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:38.131885image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:51.180386image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:04.482943image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:15.706036image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:29.354805image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:41.061533image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:53.541071image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:05.190036image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:17.193755image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:30.644618image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:39.122296image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:43.478136image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:48.329283image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:53.408748image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:59.079713image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:04.913866image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:09.183554image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:12:55.163567image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:00.473923image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:04.877211image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:09.912562image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:15.323331image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:19.885804image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:24.015075image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:28.244658image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:38.622526image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:51.744781image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:04.991153image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:16.212132image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:29.835175image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:41.582841image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:54.026910image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:05.649025image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:18.710578image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:31.239381image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:39.331892image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:43.660070image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:48.586660image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:53.569108image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:59.312489image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:05.028241image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:09.431964image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:12:55.358617image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:00.679228image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:05.078169image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
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2024-09-29T16:13:15.536785image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
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2024-09-29T16:13:24.176213image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:28.387136image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:39.219239image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:52.348643image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:05.449208image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:16.781732image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:30.345273image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:42.152916image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:54.474728image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:06.171639image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:19.397513image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:31.752313image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:39.522086image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:43.869126image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:48.815594image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:53.757004image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:59.518022image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:05.181080image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:09.681190image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:12:55.574728image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:00.797715image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:05.238533image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:10.174702image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:15.710396image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:20.232195image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
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2024-09-29T16:13:27.159202image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:35.213839image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:48.261952image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:01.355539image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:12.852393image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:25.433958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:38.135268image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:50.541140image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:02.377303image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:14.370013image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:27.713237image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:38.095516image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:42.352301image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:47.179102image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:52.222019image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:57.909443image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:03.420727image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:07.716626image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:12.766279image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:12:59.260037image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:03.847255image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:08.758568image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:13.838713image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:19.034964image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:23.063472image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:27.279417image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:35.711741image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:48.754876image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:01.836197image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:13.261427image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:25.852719image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:38.581876image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:51.011890image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:02.824467image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:14.834506image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:28.197757image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:38.241436image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:42.499665image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:47.407956image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:52.421281image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:58.066629image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:03.702621image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:07.841152image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:12.953127image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:12:59.470570image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:03.982194image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:08.940724image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:14.093879image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:19.175142image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:23.235953image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:27.428609image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:36.172544image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:49.211001image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:02.391531image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:13.821958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:26.398860image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:39.129260image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:51.518720image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:03.300353image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:15.299516image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:28.653903image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:38.416367image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:42.690210image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:47.567502image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:52.672220image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:58.208022image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:03.978661image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:07.978389image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:13.191291image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:12:59.692523image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:04.170820image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:09.171151image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:14.350364image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:19.306486image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:23.359494image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:27.551227image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:36.674225image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:49.653749image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:02.895130image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:14.243541image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:27.927141image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:39.633520image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:52.037614image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:03.785357image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:15.796457image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:29.115419image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:38.575034image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:42.867453image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:47.789874image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:52.927526image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:58.375711image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:04.254996image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:08.212644image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:13.448658image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:12:59.843398image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:04.330675image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:09.374174image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:14.607193image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:19.502110image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:23.500335image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:27.705722image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:37.146048image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:13:50.143945image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:03.410512image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:14.676311image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:28.373175image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:40.097434image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:14:52.543200image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:04.202613image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:16.244276image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:29.600474image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:38.724272image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:43.032413image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:47.960899image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:53.125939image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:15:58.639240image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:04.515741image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-29T16:16:08.474152image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2024-09-29T16:16:50.124696image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
1stFlrSF2ndFlrSF3SsnPorchBedroomAbvGrBldgTypeBsmtCondBsmtExposureBsmtFinSF1BsmtFinSF2BsmtFinType1BsmtFinType2BsmtFullBathBsmtHalfBathBsmtQualBsmtUnfSFCentralAirCondition1Condition2ElectricalEnclosedPorchExterCondExterQualExterior1stExterior2ndFireplaceQuFireplacesFoundationFullBathFunctionalGarageAreaGarageCarsGarageCondGarageFinishGarageQualGarageTypeGarageYrBlt_AgeGrLivAreaHalfBathHeatingHeatingQCHouseStyleKitchenAbvGrKitchenQualLandContourLandSlopeLotAreaLotConfigLotFrontageLotShapeLowQualFinSFMSSubClassMSZoningMasVnrAreaMasVnrTypeMiscValMoSoldNeighborhoodOpenPorchSFOverallCondOverallQualPavedDriveRoofMatlRoofStyleSaleConditionSalePriceSaleTypeScreenPorchTotRmsAbvGrdTotalBsmtSFWoodDeckSFYearBuilt_AgeYearRemodAdd_AgeYrSold_Age
1stFlrSF1.000-0.2840.0580.1350.1580.0000.1590.3140.0580.0970.0650.1560.0000.2200.2330.1530.0650.2110.000-0.1300.0000.2800.2550.1190.1910.2530.1030.2760.0370.4900.2540.0630.2210.0680.163-0.2230.4870.1150.0000.1120.1520.0000.2610.0480.0000.4330.0410.3850.088-0.0430.2060.1590.3530.220-0.0460.0560.2270.2330.0750.2370.1010.2160.1410.1140.5680.0880.1080.3600.8310.214-0.294-0.2400.033
2ndFlrSF-0.2841.000-0.0180.5070.1320.0740.129-0.197-0.1100.1100.0360.1610.0000.1800.0600.0460.0620.1050.0000.0400.0000.2080.1030.1060.1610.1400.1660.3990.0260.0940.2270.0530.1760.1000.223-0.0800.6430.4530.0650.1160.4330.0310.1750.0560.0200.1170.0460.0520.1460.0500.3920.1640.0640.122-0.0280.0430.2380.2200.0920.1960.1100.1070.1430.0450.2950.0090.0100.583-0.2960.075-0.031-0.0740.000
3SsnPorch0.058-0.0181.000-0.0190.0000.0000.0000.056-0.0130.0200.0000.0000.0620.0000.0040.0000.1660.0000.000-0.0380.0000.0340.0000.0000.0000.0000.1350.0000.0000.0360.0000.0000.0000.0280.000-0.0170.0360.0000.0000.0000.0000.0000.0000.0660.0700.0610.0440.0450.0000.0250.0000.0000.0460.0000.0120.0410.0000.0160.0060.0000.0000.1510.0000.0000.0670.000-0.0370.0050.046-0.023-0.020-0.0540.000
BedroomAbvGr0.1350.507-0.0191.0000.3060.1040.104-0.0890.0030.1070.0330.3520.0320.0960.1620.1650.0600.0000.067-0.0010.0000.1790.0810.0680.0850.1000.0860.4450.0290.1100.1350.0750.1280.0850.1520.0510.5390.2540.0660.0210.2430.2370.1340.1160.1300.3360.0000.2890.0340.0140.3420.1660.1160.056-0.0050.0480.2070.0970.0740.1400.1000.1110.1900.1120.2290.0570.0350.6660.0560.0590.0340.0560.014
BldgType0.1580.1320.0000.3061.0000.1580.1630.0360.0000.1830.1530.1990.0660.2090.1220.2840.0740.1340.0800.0590.0980.1640.1630.1880.0970.1250.1930.1050.0420.1500.1500.1020.1860.1180.1790.1800.1040.2150.1120.1140.1580.4930.1430.0740.0030.2350.0680.3950.0870.0780.8910.1880.0000.0880.0000.0070.4190.0000.1310.1260.1400.0400.0050.1490.0900.1010.0000.1990.2090.0840.2530.1910.000
BsmtCond0.0000.0740.0000.1040.1581.0000.4950.0810.0000.5070.4940.1030.0620.5270.1400.3070.0420.0000.3780.0930.1860.1680.1400.1100.0430.0500.4140.1400.1980.0760.0970.1770.1220.2130.1130.1490.0380.0950.1900.0990.0800.1780.1360.0650.1630.0000.0140.0870.0650.0750.2030.0790.0000.0580.0640.0000.1300.0000.4140.3080.1650.0520.0510.0970.1520.0730.0000.0690.4510.0250.1900.1170.051
BsmtExposure0.1590.1290.0000.1040.1630.4951.0000.2300.0450.5210.4910.2420.0460.5210.1680.1980.0580.0000.1070.0550.0200.1650.1510.1480.0880.1240.4070.0930.0480.1490.1440.0600.1550.0560.1370.1450.0980.1070.1770.0910.2010.1670.1480.1780.1930.1260.0470.0850.0930.0000.2640.0640.0780.1280.0530.0080.2380.0250.1040.2110.1030.1210.1190.1150.2120.0930.0140.0830.4770.1650.1860.1530.028
BsmtFinSF10.314-0.1970.056-0.0890.0360.0810.2301.0000.0400.3720.1200.5050.0670.250-0.5740.1640.0580.2230.031-0.1510.0000.2300.1150.1100.1430.1560.1710.1970.0000.2400.2080.0410.1890.0510.121-0.0750.0470.0220.0000.0830.1120.0520.2250.1220.1030.1590.0560.1450.097-0.0880.1320.0860.2400.174-0.008-0.0110.1840.0820.0710.1730.1420.0920.1000.1190.2950.1270.071-0.0570.4020.174-0.188-0.0610.000
BsmtFinSF20.058-0.110-0.0130.0030.0000.0000.0450.0401.0000.1730.4270.1020.0870.048-0.2650.0000.0000.0000.0000.0350.0000.0430.0740.0650.0600.0950.0680.0170.120-0.0130.0000.0000.0190.0000.0500.148-0.0650.0000.0000.0000.0000.0000.0430.0000.1110.0580.0000.0400.012-0.0110.0000.000-0.0650.103-0.000-0.0250.128-0.0680.0000.0000.0000.1780.1430.000-0.0490.0910.048-0.0700.0610.0660.1100.1240.029
BsmtFinType10.0970.1100.0200.1070.1830.5070.5210.3720.1731.0000.4460.4210.0850.5770.2870.2500.0530.0160.1220.0930.0710.2980.2160.2130.1230.1150.4530.2220.0930.1490.2140.0780.2250.0800.1560.2800.1050.1020.1670.2080.1490.1690.2810.0900.0460.0190.0560.0760.0660.0590.2360.1350.0830.1970.0740.0070.2920.0720.1650.2400.1890.0450.0520.1250.2110.0940.0520.0960.3890.1140.3300.2500.000
BsmtFinType20.0650.0360.0000.0330.1530.4940.4910.1200.4270.4461.0000.1290.0930.5000.1460.1850.0260.0000.0720.0160.0000.1090.1460.1160.0500.0560.3660.0480.0870.0000.0590.0460.0820.0410.0730.1230.0480.0850.1510.0970.0600.1550.0820.0000.0790.0740.0000.0480.0680.0000.1460.0380.0000.0610.0000.0000.1530.0250.0750.1350.0860.1050.0650.0720.1070.0540.0000.0410.3630.0510.1490.1250.000
BsmtFullBath0.1560.1610.0000.3520.1990.1030.2420.5050.1020.4210.1291.0000.1020.1550.3250.1060.0280.0000.0730.0220.0030.0910.1030.0910.0680.1010.1280.3370.0000.1280.1300.0710.1220.0780.1500.1510.0240.1810.0000.0810.2150.1820.1140.1160.1370.1470.0340.0960.0480.0000.3050.0910.0490.1020.0770.0000.2430.0690.0360.0840.0890.1250.2280.1930.1740.1720.0610.0740.1990.2350.1850.1420.057
BsmtHalfBath0.0000.0000.0620.0320.0660.0620.0460.0670.0870.0850.0930.1021.0000.0530.0550.0140.0000.0000.0000.0000.0560.0520.0660.0680.0260.0000.0690.1650.0000.0300.0720.0270.0610.0320.0260.0770.0000.1610.0000.0080.1020.4980.0000.0180.0340.0000.0330.0000.0360.0000.0890.0180.0000.0390.0000.0080.1420.0000.1040.0650.0150.1600.1400.2530.0800.0050.0000.0000.0000.0000.0940.0850.022
BsmtQual0.2200.1800.0000.0960.2090.5270.5210.2500.0480.5770.5000.1550.0531.0000.2080.2750.1330.0920.2000.1530.0970.4670.3020.2860.2440.1800.5310.3410.1040.2980.3480.1500.3410.1640.2430.4010.2150.1760.1820.2420.1880.1800.4190.0960.0000.0320.0690.1600.1360.0330.3370.1660.1770.2530.0600.0000.4700.1320.2820.4570.1920.0000.1380.2260.4090.2100.0000.1620.5280.1750.4590.3430.000
BsmtUnfSF0.2330.0600.0040.1620.1220.1400.168-0.574-0.2650.2870.1460.3250.0550.2081.0000.0610.0190.0700.0170.0440.0240.2560.0950.1040.1410.0950.1690.1930.0530.1120.1790.0440.1290.0730.095-0.1880.2600.1220.0000.0970.1500.0640.1950.0660.0500.0860.0220.1050.0480.0260.1600.0720.0770.168-0.0370.0360.1930.1520.0700.1590.0940.0000.0910.1320.1920.092-0.0090.2680.337-0.032-0.140-0.1770.036
CentralAir0.1530.0460.0000.1650.2840.3070.1980.1640.0000.2500.1850.1060.0140.2750.0611.0000.0460.0730.4230.2300.1850.2650.3560.3370.1930.1930.3640.1050.0660.2710.2770.3490.3210.3150.3570.3420.1800.1310.4690.3750.2350.2370.3340.1310.0000.0550.0630.1940.1070.1260.4350.2940.1050.1780.0000.0000.3770.1030.3190.3720.3310.0000.0570.1140.4130.1280.0000.1150.2410.1510.4450.3850.000
Condition10.0650.0620.1660.0600.0740.0420.0580.0580.0000.0530.0260.0280.0000.1330.0190.0461.0000.2270.0210.0240.0000.1310.0760.0830.0000.0500.0840.0640.0000.0670.0650.0340.1220.0350.0920.1080.0650.0700.0000.1600.0830.0630.0880.0000.0000.0360.1470.0460.1110.0000.1060.0740.0000.0510.0000.0000.1860.0620.0520.0630.1000.0690.0650.0000.0660.0360.0000.0640.0540.0600.1220.0900.000
Condition20.2110.1050.0000.0000.1340.0000.0000.2230.0000.0160.0000.0000.0000.0920.0700.0730.2271.0000.0000.0000.2850.1430.0290.0000.0000.0180.0410.1150.0000.1420.0000.0000.0000.1510.0950.0760.2090.0000.0000.0740.1310.1060.0940.0660.0000.1490.0950.0610.0000.0950.1700.0640.1160.0000.0000.0000.0240.3040.1120.1770.0000.0000.0000.0000.0180.0050.0000.0680.1500.0000.1770.0000.000
Electrical0.0000.0000.0000.0670.0800.3780.1070.0310.0000.1220.0720.0730.0000.2000.0170.4230.0210.0001.0000.0590.1280.1390.1800.1570.0510.0830.1690.1130.1950.1020.1250.2110.1480.3070.1160.1980.0450.0830.1150.1460.1080.1060.2030.0460.0000.0000.0000.0470.1220.0000.1630.0990.0000.0780.0000.0000.1690.0000.2400.1400.1930.0000.0000.1410.1160.0000.0000.0630.1000.0390.1670.2030.000
EnclosedPorch-0.1300.040-0.038-0.0010.0590.0930.055-0.1510.0350.0930.0160.0220.0000.1530.0440.2300.0240.0000.0591.0000.0000.0900.1720.1650.0670.0410.2730.1080.013-0.1850.2510.1020.1350.1000.0990.298-0.0540.0760.1210.1380.1360.0560.0860.0570.000-0.0690.039-0.1010.0540.0330.1730.153-0.1800.1070.048-0.0280.137-0.1710.0710.0350.2080.0000.1640.056-0.2220.107-0.080-0.033-0.176-0.1590.4110.2360.000
ExterCond0.0000.0000.0000.0000.0980.1860.0200.0000.0000.0710.0000.0030.0560.0970.0240.1850.0000.2850.1280.0001.0000.1640.0980.0660.0050.0330.1170.0800.1570.1070.1240.1230.1370.1630.1070.1110.0660.0450.0480.0520.1050.0000.1680.0000.0000.0000.0000.0000.0000.0900.1590.0780.0000.0610.1270.0000.1450.1600.3810.1970.1450.0000.0910.0520.1010.0920.0000.0000.0430.0390.1870.1020.010
ExterQual0.2800.2080.0340.1790.1640.1680.1650.2300.0430.2980.1090.0910.0520.4670.2560.2650.1310.1430.1390.0900.1641.0000.3530.3570.2390.1840.3680.3240.1020.3360.3630.1460.3400.1430.2920.4180.2960.1520.0440.3230.1800.0800.5440.1430.1050.0510.0140.1510.1160.0970.2830.2430.2370.2470.0230.0440.4870.1740.3250.6240.1830.0000.1480.2370.4850.2620.0250.2760.3380.1770.4380.3820.036
Exterior1st0.2550.1030.0000.0810.1630.1400.1510.1150.0740.2160.1460.1030.0660.3020.0950.3560.0760.0290.1800.1720.0980.3531.0000.7580.1730.1310.3180.2400.0970.1400.2460.1310.2850.1180.1960.2840.0960.1130.1350.2690.1600.1580.2940.1160.1190.0350.0560.0980.0790.0000.1950.1800.0020.2240.0000.0000.2910.0770.1880.2020.1960.1920.1350.1710.1660.1200.0500.0950.1230.0990.3340.2830.043
Exterior2nd0.1190.1060.0000.0680.1880.1100.1480.1100.0650.2130.1160.0910.0680.2860.1040.3370.0830.0000.1570.1650.0660.3570.7581.0000.1360.1010.3150.2280.0820.1350.2340.1000.2750.0980.1940.2860.0900.1660.1810.2680.1690.1300.2870.1160.1040.0780.0710.1210.0840.0000.2060.1870.0250.2150.0000.0000.3180.0800.1680.1940.1780.1060.1550.1630.1600.1190.0760.0970.1190.0980.3240.2780.031
FireplaceQu0.1910.1610.0000.0850.0970.0430.0880.1430.0600.1230.0500.0680.0260.2440.1410.1930.0000.0000.0510.0670.0050.2390.1730.1361.0000.5810.1140.1920.1010.1820.2270.0960.2340.0950.1820.2030.2310.1720.0000.1190.1130.0760.2600.0670.0120.0950.0520.1360.1210.0000.1880.1180.1600.2000.0000.0270.3050.0590.0890.2730.1220.0620.0850.1200.2780.1270.0550.1750.1950.1260.2150.2280.000
Fireplaces0.2530.1400.0000.1000.1250.0500.1240.1560.0950.1150.0560.1010.0000.1800.0950.1930.0500.0180.0830.0410.0330.1840.1310.1010.5811.0000.1200.1780.0000.1740.2020.1160.2270.1230.2320.1550.2750.1670.0000.0970.1010.0870.1770.0370.1070.1700.0480.1440.1160.0000.2220.1330.1460.1310.0000.0000.3030.0990.1050.2610.1080.0880.0820.0910.2870.0790.1200.2090.2050.1440.1690.1460.037
Foundation0.1030.1660.1350.0860.1930.4140.4070.1710.0680.4530.3660.1280.0690.5310.1690.3640.0840.0410.1690.2730.1170.3680.3180.3150.1140.1201.0000.2880.0900.1870.2720.1370.3090.1970.2370.4260.1490.1610.2220.2920.2160.1610.3440.1030.0470.0000.0470.2270.1240.0310.3600.2270.0740.2070.0990.0000.4180.1280.2550.2900.2360.0000.0970.1630.2590.1490.0000.1190.3520.1400.5080.3320.033
FullBath0.2760.3990.0000.4450.1050.1400.0930.1970.0170.2220.0480.3370.1650.3410.1930.1050.0640.1150.1130.1080.0800.3240.2400.2280.1920.1780.2881.0000.0680.2760.3290.0910.2680.0920.2710.3360.4700.2390.0000.2020.2370.1120.2780.1170.1560.1900.0440.1360.0990.0000.3140.1760.1850.1760.0410.0380.3720.1680.3110.4030.1010.1090.1950.1880.4100.1360.0000.3900.2430.2500.3550.2730.000
Functional0.0370.0260.0000.0290.0420.1980.0480.0000.1200.0930.0870.0000.0000.1040.0530.0660.0000.0000.1950.0130.1570.1020.0970.0820.1010.0000.0900.0681.0000.0000.0380.0640.0730.1100.1480.0760.0630.0340.0650.0290.0390.0000.0770.0000.0980.1090.0000.0480.0000.0740.0920.0000.0000.1740.0990.0340.0840.0870.1690.1140.0620.1700.1380.0320.0340.0230.0940.0260.0000.0740.0830.0580.041
GarageArea0.4900.0940.0360.1100.1500.0760.1490.240-0.0130.1490.0000.1280.0300.2980.1120.2710.0670.1420.102-0.1850.1070.3360.1400.1350.1820.1740.1870.2760.0001.0000.7720.4550.6240.4620.448-0.5490.4670.1540.0510.1420.1220.0870.3350.1090.0580.3670.0510.3500.118-0.0570.1950.1760.3660.242-0.0340.0330.2570.3400.1380.2540.2690.0000.0680.1570.6540.1280.0310.3310.4860.248-0.530-0.4010.000
GarageCars0.2540.2270.0000.1350.1500.0970.1440.2080.0000.2140.0590.1300.0720.3480.1790.2770.0650.0000.1250.2510.1240.3630.2460.2340.2270.2020.2720.3290.0380.7721.0000.5050.6360.5080.5370.4400.2850.1990.0920.1790.1690.1210.3610.0920.0230.1480.0440.2000.1160.0880.2950.1450.2020.2600.0000.0000.3900.1290.2200.4040.2610.0000.1330.2170.4180.1920.0000.2470.2840.1500.3410.2710.000
GarageCond0.0630.0530.0000.0750.1020.1770.0600.0410.0000.0780.0460.0710.0270.1500.0440.3490.0340.0000.2110.1020.1230.1460.1310.1000.0960.1160.1370.0910.0640.4550.5051.0000.5870.6980.4600.3410.1030.1150.1680.0930.1280.1860.1830.0000.0000.0000.0400.0490.0460.0970.2100.1000.0000.1090.0590.0250.1700.0000.1350.1620.3020.0460.0280.0790.1830.0430.0000.0850.0740.0460.2000.1080.000
GarageFinish0.2210.1760.0000.1280.1860.1220.1550.1890.0190.2250.0820.1220.0610.3410.1290.3210.1220.0000.1480.1350.1370.3400.2850.2750.2340.2270.3090.2680.0730.6240.6360.5871.0000.5890.6850.4650.2440.2000.1130.2420.2090.1390.3180.1040.0000.1150.0320.1620.1400.1120.3830.1940.1580.2210.0000.0000.4170.1470.2330.3720.2690.0000.0950.1880.3920.1710.0130.2030.2460.1890.3910.2870.000
GarageQual0.0680.1000.0280.0850.1180.2130.0560.0510.0000.0800.0410.0780.0320.1640.0730.3150.0350.1510.3070.1000.1630.1430.1180.0980.0950.1230.1970.0920.1100.4620.5080.6980.5891.0000.4590.3490.1550.1080.1280.0900.1440.1250.1820.0410.0000.0730.0000.0770.0760.1620.2350.1250.0000.1210.0540.0000.1970.0850.1640.1600.2810.0840.0000.0810.2220.0250.1560.1210.0750.0520.2370.1140.026
GarageType0.1630.2230.0000.1520.1790.1130.1370.1210.0500.1560.0730.1500.0260.2430.0950.3570.0920.0950.1160.0990.1070.2920.1960.1940.1820.2320.2370.2710.1480.4480.5370.4600.6850.4591.0000.3060.1880.2380.0980.1600.2040.1770.2630.1200.1130.1600.0550.1640.1470.0800.3340.2120.0940.2170.0000.0240.3000.0900.1690.2260.2880.0030.0660.1460.2500.1010.0180.1770.1780.1240.2660.1920.000
GarageYrBlt_Age-0.223-0.080-0.0170.0510.1800.1490.145-0.0750.1480.2800.1230.1510.0770.401-0.1880.3420.1080.0760.1980.2980.1110.4180.2840.2860.2030.1550.4260.3360.076-0.5490.4400.3410.4650.3490.3061.000-0.2850.1950.1220.3310.2280.1960.3900.1330.087-0.0460.087-0.1040.1520.0160.2940.230-0.2980.2760.056-0.0140.400-0.3900.2120.2320.2380.0380.1330.206-0.5800.1610.100-0.198-0.326-0.2770.8500.7040.000
GrLivArea0.4870.6430.0360.5390.1040.0380.0980.047-0.0650.1050.0480.0240.0000.2150.2600.1800.0650.2090.045-0.0540.0660.2960.0960.0900.2310.2750.1490.4700.0630.4670.2850.1030.2440.1550.188-0.2851.0000.2920.0910.1340.2940.0610.2680.0770.0000.4420.0750.3380.1170.0570.2890.1210.3250.147-0.0690.0810.2390.3960.1230.2770.1240.1420.0700.0860.7290.0460.0860.8280.3660.229-0.292-0.2840.044
HalfBath0.1150.4530.0000.2540.2150.0950.1070.0220.0000.1020.0850.1810.1610.1760.1220.1310.0700.0000.0830.0760.0450.1520.1130.1660.1720.1670.1610.2390.0340.1540.1990.1150.2000.1080.2380.1950.2921.0000.0000.1010.4630.1790.1450.0190.0570.0640.0210.1290.0880.0000.5270.1400.1370.0970.0000.0580.2990.1480.0760.2240.0810.0100.1610.1450.2110.0330.0510.2560.1500.0850.2220.2030.000
Heating0.0000.0650.0000.0660.1120.1900.1770.0000.0000.1670.1510.0000.0000.1820.0000.4690.0000.0000.1150.1210.0480.0440.1350.1810.0000.0000.2220.0000.0650.0510.0920.1680.1130.1280.0980.1220.0910.0001.0000.2440.1430.0910.1580.0000.0000.0000.0000.1080.0000.3080.1560.0570.0000.0000.0000.0000.0560.0790.0950.1670.1510.0000.0000.0000.0820.0650.0850.0350.1360.0000.1670.0910.028
HeatingQC0.1120.1160.0000.0210.1140.0990.0910.0830.0000.2080.0970.0810.0080.2420.0970.3750.1600.0740.1460.1380.0520.3230.2690.2680.1190.0970.2920.2020.0290.1420.1790.0930.2420.0900.1600.3310.1340.1010.2441.0000.1690.0970.3200.0560.0520.0000.0250.0660.0590.0630.2410.1170.0360.1570.0000.0210.2980.1060.1810.2620.1760.0000.0000.1480.2410.1320.0000.0990.1400.1190.3370.3330.000
HouseStyle0.1520.4330.0000.2430.1580.0800.2010.1120.0000.1490.0600.2150.1020.1880.1500.2350.0830.1310.1080.1360.1050.1800.1600.1690.1130.1010.2160.2370.0390.1220.1690.1280.2090.1440.2040.2280.2940.4630.1430.1691.0000.1490.1460.1280.0000.0000.0000.1350.0790.2700.8530.1860.0480.1650.0000.0000.2970.1270.1230.1430.1630.0520.0980.0910.1310.0550.0740.2700.1830.0520.2910.2060.000
KitchenAbvGr0.0000.0310.0000.2370.4930.1780.1670.0520.0000.1690.1550.1820.4980.1800.0640.2370.0630.1060.1060.0560.0000.0800.1580.1300.0760.0870.1610.1120.0000.0870.1210.1860.1390.1250.1770.1960.0610.1790.0910.0970.1491.0000.0990.0000.0000.0000.0420.0740.0390.0000.4990.0910.0000.0000.0000.0140.1040.0000.0760.1030.1240.1950.1680.3300.0450.0000.0000.1720.1470.0000.2140.1140.000
KitchenQual0.2610.1750.0000.1340.1430.1360.1480.2250.0430.2810.0820.1140.0000.4190.1950.3340.0880.0940.2030.0860.1680.5440.2940.2870.2600.1770.3440.2780.0770.3350.3610.1830.3180.1820.2630.3900.2680.1450.1580.3200.1460.0991.0000.1000.0650.0610.0000.1460.0920.0650.2810.1750.1820.2200.0330.0310.4470.1560.2500.5370.1850.0000.1110.2120.4620.2080.0270.2350.3000.1890.4040.4180.000
LandContour0.0480.0560.0660.1160.0740.0650.1780.1220.0000.0900.0000.1160.0180.0960.0660.1310.0000.0660.0460.0570.0000.1430.1160.1160.0670.0370.1030.1170.0000.1090.0920.0000.1040.0410.1200.1330.0770.0190.0000.0560.1280.0000.1001.0000.4330.2600.0470.1300.0980.0740.1480.1040.0290.0850.0000.0710.3500.0000.1080.1670.1200.1810.1750.1210.0880.0390.0000.0680.0960.1100.1660.1300.000
LandSlope0.0000.0200.0700.1300.0030.1630.1930.1030.1110.0460.0790.1370.0340.0000.0500.0000.0000.0000.0000.0000.0000.1050.1190.1040.0120.1070.0470.1560.0980.0580.0230.0000.0000.0000.1130.0870.0000.0570.0000.0520.0000.0000.0650.4331.0000.3700.0490.1120.0630.0540.1110.0700.0000.0190.0000.0230.2850.0000.2290.1790.0000.3800.3660.0590.0320.0000.0910.0790.0000.0910.0970.0760.000
LotArea0.4330.1170.0610.3360.2350.0000.1260.1590.0580.0190.0740.1470.0000.0320.0860.0550.0360.1490.000-0.0690.0000.0510.0350.0780.0950.1700.0000.1900.1090.3670.1480.0000.1150.0730.160-0.0460.4420.0640.0000.0000.0000.0000.0610.2600.3701.0000.1070.5680.244-0.0280.1720.1910.1780.1060.0370.0080.2120.1760.0000.0620.0960.3210.1800.0000.4490.0000.0910.4060.3600.181-0.105-0.0730.000
LotConfig0.0410.0460.0440.0000.0680.0140.0470.0560.0000.0560.0000.0340.0330.0690.0220.0630.1470.0950.0000.0390.0000.0140.0560.0710.0520.0480.0470.0440.0000.0510.0440.0400.0320.0000.0550.0870.0750.0210.0000.0250.0000.0420.0000.0470.0490.1071.0000.1450.2170.0000.0790.0630.0360.0000.0000.0300.1350.0000.0020.0190.0250.0730.0680.0250.0820.0000.0400.0000.0420.0340.1060.0820.039
LotFrontage0.3850.0520.0450.2890.3950.0870.0850.1450.0400.0760.0480.0960.0000.1600.1050.1940.0460.0610.047-0.1010.0000.1510.0980.1210.1360.1440.2270.1360.0480.3500.2000.0490.1620.0770.164-0.1040.3380.1290.1080.0660.1350.0740.1460.1300.1120.5680.1451.0000.264-0.0420.3050.2380.2600.1680.0210.0240.2990.1630.0630.1270.1550.0000.0830.0800.3880.0330.0390.3230.3520.109-0.193-0.1050.023
LotShape0.0880.1460.0000.0340.0870.0650.0930.0970.0120.0660.0680.0480.0360.1360.0480.1070.1110.0000.1220.0540.0000.1160.0790.0840.1210.1160.1240.0990.0000.1180.1160.0460.1400.0760.1470.1520.1170.0880.0000.0590.0790.0390.0920.0980.0630.2440.2170.2641.0000.0000.1660.1520.0590.0730.0000.0000.2430.0570.0630.1100.0780.0140.0000.0150.2010.0000.0470.0840.0840.1060.1790.1400.000
LowQualFinSF-0.0430.0500.0250.0140.0780.0750.000-0.088-0.0110.0590.0000.0000.0000.0330.0260.1260.0000.0950.0000.0330.0900.0970.0000.0000.0000.0000.0310.0000.074-0.0570.0880.0970.1120.1620.0800.0160.0570.0000.3080.0630.2700.0000.0650.0740.054-0.0280.000-0.0420.0001.0000.2250.146-0.1060.0000.0400.0030.1130.0110.0790.0250.0920.0600.0000.000-0.0740.000-0.0170.038-0.088-0.0380.1410.0650.022
MSSubClass0.2060.3920.0000.3420.8910.2030.2640.1320.0000.2360.1460.3050.0890.3370.1600.4350.1060.1700.1630.1730.1590.2830.1950.2060.1880.2220.3600.3140.0920.1950.2950.2100.3830.2350.3340.2940.2890.5270.1560.2410.8530.4990.2810.1480.1110.1720.0790.3050.1660.2251.0000.3390.0680.2290.0310.0090.3750.1260.1900.2020.3100.0910.2640.1480.2160.0920.0000.2810.2490.1020.3860.2520.000
MSZoning0.1590.1640.0000.1660.1880.0790.0640.0860.0000.1350.0380.0910.0180.1660.0720.2940.0740.0640.0990.1530.0780.2430.1800.1870.1180.1330.2270.1760.0000.1760.1450.1000.1940.1250.2120.2300.1210.1400.0570.1170.1860.0910.1750.1040.0700.1910.0630.2380.1520.1460.3391.0000.0610.0990.0000.0260.6410.1460.1630.1900.2180.0000.0730.1380.2050.1510.0000.1740.1670.0680.2940.1950.000
MasVnrArea0.3530.0640.0460.1160.0000.0000.0780.240-0.0650.0830.0000.0490.0000.1770.0770.1050.0000.1160.000-0.1800.0000.2370.0020.0250.1600.1460.0740.1850.0000.3660.2020.0000.1580.0000.094-0.2980.3250.1370.0000.0360.0480.0000.1820.0290.0000.1780.0360.2600.059-0.1060.0680.0611.0000.403-0.0640.0220.1850.2070.0370.1890.0730.1240.1070.0520.4270.0320.0330.2690.3600.170-0.403-0.2340.032
MasVnrType0.2200.1220.0000.0560.0880.0580.1280.1740.1030.1970.0610.1020.0390.2530.1680.1780.0510.0000.0780.1070.0610.2470.2240.2150.2000.1310.2070.1760.1740.2420.2600.1090.2210.1210.2170.2760.1470.0970.0000.1570.1650.0000.2200.0850.0190.1060.0000.1680.0730.0000.2290.0990.4031.0000.0000.0000.3810.1070.1650.2780.1470.0390.1320.1960.2480.1840.0000.1510.2450.1320.3040.2770.022
MiscVal-0.046-0.0280.012-0.0050.0000.0640.053-0.008-0.0000.0740.0000.0770.0000.060-0.0370.0000.0000.0000.0000.0480.1270.0230.0000.0000.0000.0000.0990.0410.099-0.0340.0000.0590.0000.0540.0000.056-0.0690.0000.0000.0000.0000.0000.0330.0000.0000.0370.0000.0210.0000.0400.0310.000-0.0640.0001.0000.0090.000-0.0330.0460.0000.0000.0000.0000.000-0.0850.172-0.003-0.042-0.0810.0200.0840.1100.000
MoSold0.0560.0430.0410.0480.0070.0000.008-0.011-0.0250.0070.0000.0000.0080.0000.0360.0000.0000.0000.000-0.0280.0000.0440.0000.0000.0270.0000.0000.0380.0340.0330.0000.0250.0000.0000.024-0.0140.0810.0580.0000.0210.0000.0140.0310.0710.0230.0080.0300.0240.0000.0030.0090.0260.0220.0000.0091.0000.0540.0670.0250.0610.0000.0000.0070.0520.0700.0320.0220.0360.0370.039-0.021-0.0250.156
Neighborhood0.2270.2380.0000.2070.4190.1300.2380.1840.1280.2920.1530.2430.1420.4700.1930.3770.1860.0240.1690.1370.1450.4870.2910.3180.3050.3030.4180.3720.0840.2570.3900.1700.4170.1970.3000.4000.2390.2990.0560.2980.2970.1040.4470.3500.2850.2120.1350.2990.2430.1130.3750.6410.1850.3810.0000.0541.0000.1220.2230.3220.3080.1170.2050.2230.3160.1700.0430.2060.2370.1950.4820.3940.000
OpenPorchSF0.2330.2200.0160.0970.0000.0000.0250.082-0.0680.0720.0250.0690.0000.1320.1520.1030.0620.3040.000-0.1710.1600.1740.0770.0800.0590.0990.1280.1680.0870.3400.1290.0000.1470.0850.090-0.3900.3960.1480.0790.1060.1270.0000.1560.0000.0000.1760.0000.1630.0570.0110.1260.1460.2070.107-0.0330.0670.1221.0000.0540.1330.0330.0000.0000.0720.4810.0560.0020.2830.2700.124-0.394-0.3550.000
OverallCond0.0750.0920.0060.0740.1310.4140.1040.0710.0000.1650.0750.0360.1040.2820.0700.3190.0520.1120.2400.0710.3810.3250.1880.1680.0890.1050.2550.3110.1690.1380.2200.1350.2330.1640.1690.2120.1230.0760.0950.1810.1230.0760.2500.1080.2290.0000.0020.0630.0630.0790.1900.1630.0370.1650.0460.0250.2230.0541.0000.3280.1920.0000.0470.1160.1620.1040.0240.0630.1010.0610.2500.1680.052
OverallQual0.2370.1960.0000.1400.1260.3080.2110.1730.0000.2400.1350.0840.0650.4570.1590.3720.0630.1770.1400.0350.1970.6240.2020.1940.2730.2610.2900.4030.1140.2540.4040.1620.3720.1600.2260.2320.2770.2240.1670.2620.1430.1030.5370.1670.1790.0620.0190.1270.1100.0250.2020.1900.1890.2780.0000.0610.3220.1330.3281.0000.1720.0640.1170.1520.3850.1620.0000.1960.2690.1150.2620.2200.000
PavedDrive0.1010.1100.0000.1000.1400.1650.1030.1420.0000.1890.0860.0890.0150.1920.0940.3310.1000.0000.1930.2080.1450.1830.1960.1780.1220.1080.2360.1010.0620.2690.2610.3020.2690.2810.2880.2380.1240.0810.1510.1760.1630.1240.1850.1200.0000.0960.0250.1550.0780.0920.3100.2180.0730.1470.0000.0000.3080.0330.1920.1721.0000.0000.1080.1070.2190.0700.0000.0960.1590.0770.3500.1790.000
RoofMatl0.2160.1070.1510.1110.0400.0520.1210.0920.1780.0450.1050.1250.1600.0000.0000.0000.0690.0000.0000.0000.0000.0000.1920.1060.0620.0880.0000.1090.1700.0000.0000.0460.0000.0840.0030.0380.1420.0100.0000.0000.0520.1950.0000.1810.3800.3210.0730.0000.0140.0600.0910.0000.1240.0390.0000.0000.1170.0000.0000.0640.0001.0000.4740.0470.1790.0000.1060.0820.1560.1770.0760.0540.000
RoofStyle0.1410.1430.0000.1900.0050.0510.1190.1000.1430.0520.0650.2280.1400.1380.0910.0570.0650.0000.0000.1640.0910.1480.1350.1550.0850.0820.0970.1950.1380.0680.1330.0280.0950.0000.0660.1330.0700.1610.0000.0000.0980.1680.1110.1750.3660.1800.0680.0830.0000.0000.2640.0730.1070.1320.0000.0070.2050.0000.0470.1170.1080.4741.0000.0860.1160.0000.0720.0850.1370.0450.1570.0680.018
SaleCondition0.1140.0450.0000.1120.1490.0970.1150.1190.0000.1250.0720.1930.2530.2260.1320.1140.0000.0000.1410.0560.0520.2370.1710.1630.1200.0910.1630.1880.0320.1570.2170.0790.1880.0810.1460.2060.0860.1450.0000.1480.0910.3300.2120.1210.0590.0000.0250.0800.0150.0000.1480.1380.0520.1960.0000.0520.2230.0720.1160.1520.1070.0470.0861.0000.1690.4710.0000.0920.1380.0000.1990.2390.082
SalePrice0.5680.2950.0670.2290.0900.1520.2120.295-0.0490.2110.1070.1740.0800.4090.1920.4130.0660.0180.116-0.2220.1010.4850.1660.1600.2780.2870.2590.4100.0340.6540.4180.1830.3920.2220.250-0.5800.7290.2110.0820.2410.1310.0450.4620.0880.0320.4490.0820.3880.201-0.0740.2160.2050.4270.248-0.0850.0700.3160.4810.1620.3850.2190.1790.1160.1691.0000.1300.1010.5340.6010.355-0.661-0.5770.000
SaleType0.0880.0090.0000.0570.1010.0730.0930.1270.0910.0940.0540.1720.0050.2100.0920.1280.0360.0050.0000.1070.0920.2620.1200.1190.1270.0790.1490.1360.0230.1280.1920.0430.1710.0250.1010.1610.0460.0330.0650.1320.0550.0000.2080.0390.0000.0000.0000.0330.0000.0000.0920.1510.0320.1840.1720.0320.1700.0560.1040.1620.0700.0000.0000.4710.1301.0000.0000.0460.0930.0000.1540.1900.087
ScreenPorch0.1080.010-0.0370.0350.0000.0000.0140.0710.0480.0520.0000.0610.0000.000-0.0090.0000.0000.0000.000-0.0800.0000.0250.0500.0760.0550.1200.0000.0000.0940.0310.0000.0000.0130.1560.0180.1000.0860.0510.0850.0000.0740.0000.0270.0000.0910.0910.0400.0390.047-0.0170.0000.0000.0330.000-0.0030.0220.0430.0020.0240.0000.0000.1060.0720.0000.1010.0001.0000.0310.089-0.0930.0730.0460.000
TotRmsAbvGrd0.3600.5830.0050.6660.1990.0690.083-0.057-0.0700.0960.0410.0740.0000.1620.2680.1150.0640.0680.063-0.0330.0000.2760.0950.0970.1750.2090.1190.3900.0260.3310.2470.0850.2030.1210.177-0.1980.8280.2560.0350.0990.2700.1720.2350.0680.0790.4060.0000.3230.0840.0380.2810.1740.2690.151-0.0420.0360.2060.2830.0630.1960.0960.0820.0850.0920.5340.0460.0311.0000.2340.170-0.180-0.2010.000
TotalBsmtSF0.831-0.2960.0460.0560.2090.4510.4770.4020.0610.3890.3630.1990.0000.5280.3370.2410.0540.1500.100-0.1760.0430.3380.1230.1190.1950.2050.3520.2430.0000.4860.2840.0740.2460.0750.178-0.3260.3660.1500.1360.1400.1830.1470.3000.0960.0000.3600.0420.3520.084-0.0880.2490.1670.3600.245-0.0810.0370.2370.2700.1010.2690.1590.1560.1370.1380.6010.0930.0890.2341.0000.230-0.428-0.3020.000
WoodDeckSF0.2140.075-0.0230.0590.0840.0250.1650.1740.0660.1140.0510.2350.0000.175-0.0320.1510.0600.0000.039-0.1590.0390.1770.0990.0980.1260.1440.1400.2500.0740.2480.1500.0460.1890.0520.124-0.2770.2290.0850.0000.1190.0520.0000.1890.1100.0910.1810.0340.1090.106-0.0380.1020.0680.1700.1320.0200.0390.1950.1240.0610.1150.0770.1770.0450.0000.3550.000-0.0930.1700.2301.000-0.287-0.2350.043
YearBuilt_Age-0.294-0.031-0.0200.0340.2530.1900.186-0.1880.1100.3300.1490.1850.0940.459-0.1400.4450.1220.1770.1670.4110.1870.4380.3340.3240.2150.1690.5080.3550.083-0.5300.3410.2000.3910.2370.2660.850-0.2920.2220.1670.3370.2910.2140.4040.1660.097-0.1050.106-0.1930.1790.1410.3860.294-0.4030.3040.084-0.0210.482-0.3940.2500.2620.3500.0760.1570.199-0.6610.1540.073-0.180-0.428-0.2871.0000.6920.000
YearRemodAdd_Age-0.240-0.074-0.0540.0560.1910.1170.153-0.0610.1240.2500.1250.1420.0850.343-0.1770.3850.0900.0000.2030.2360.1020.3820.2830.2780.2280.1460.3320.2730.058-0.4010.2710.1080.2870.1140.1920.704-0.2840.2030.0910.3330.2060.1140.4180.1300.076-0.0730.082-0.1050.1400.0650.2520.195-0.2340.2770.110-0.0250.394-0.3550.1680.2200.1790.0540.0680.239-0.5770.1900.046-0.201-0.302-0.2350.6921.0000.000
YrSold_Age0.0330.0000.0000.0140.0000.0510.0280.0000.0290.0000.0000.0570.0220.0000.0360.0000.0000.0000.0000.0000.0100.0360.0430.0310.0000.0370.0330.0000.0410.0000.0000.0000.0000.0260.0000.0000.0440.0000.0280.0000.0000.0000.0000.0000.0000.0000.0390.0230.0000.0220.0000.0000.0320.0220.0000.1560.0000.0000.0520.0000.0000.0000.0180.0820.0000.0870.0000.0000.0000.0430.0000.0001.000

Missing values

2024-09-29T16:16:13.824522image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-29T16:16:14.472305image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

LotFrontageLotAreaMasVnrAreaBsmtFinSF1BsmtFinSF2BsmtUnfSFTotalBsmtSF1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrTotRmsAbvGrdFireplacesGarageCarsGarageAreaWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldSalePriceYearBuilt_AgeYearRemodAdd_AgeGarageYrBlt_AgeYrSold_AgeMSSubClassMSZoningLotShapeLandContourLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinType2HeatingHeatingQCCentralAirElectricalKitchenQualFunctionalFireplaceQuGarageTypeGarageFinishGarageQualGarageCondPavedDriveSaleTypeSaleCondition
065.0000008450.0196.0706.00.0150.0856.0856.0854.00.01710.01.00.02.01.03.01.08.00.02.0548.00.061.00.00.00.00.00.02.0208500.021.021.021.016.02-STORY 1946 & NEWERRLRegLvlInsideGtlCollgCrNormNorm1Fam2StoryGoodAverageGableCompShgVinylSdVinylSdBrkFaceGdTAPConcGdTANoGLQUnfGasAExYSBrkrGdTypNAAttchdRFnTATAYWDNormal
180.0000009600.00.0978.00.0284.01262.01262.00.00.01262.00.01.02.00.03.01.06.01.02.0460.0298.00.00.00.00.00.00.05.0181500.048.048.048.017.01-STORY 1946 & NEWER ALL STYLESRLRegLvlFR2GtlVeenkerFeedrNorm1Fam1StoryAbove AverageVery GoodGableCompShgMetalSdMetalSdNATATACBlockGdTAGdALQUnfGasAExYSBrkrTATypTAAttchdRFnTATAYWDNormal
268.00000011250.0162.0486.00.0434.0920.0920.0866.00.01786.01.00.02.01.03.01.06.01.02.0608.00.042.00.00.00.00.00.09.0223500.023.022.023.016.02-STORY 1946 & NEWERRLIR1LvlInsideGtlCollgCrNormNorm1Fam2StoryGoodAverageGableCompShgVinylSdVinylSdBrkFaceGdTAPConcGdTAMnGLQUnfGasAExYSBrkrGdTypTAAttchdRFnTATAYWDNormal
360.0000009550.00.0216.00.0540.0756.0961.0756.00.01717.01.00.01.00.03.01.07.01.03.0642.00.035.0272.00.00.00.00.02.0140000.0109.054.026.018.02-STORY 1945 & OLDERRLIR1LvlCornerGtlCrawforNormNorm1Fam2StoryGoodAverageGableCompShgWd SdngWd ShngNATATABrkTilTAGdNoALQUnfGasAGdYSBrkrGdTypGdDetchdUnfTATAYWDAbnorml
484.00000014260.0350.0655.00.0490.01145.01145.01053.00.02198.01.00.02.01.04.01.09.01.03.0836.0192.084.00.00.00.00.00.012.0250000.024.024.024.016.02-STORY 1946 & NEWERRLIR1LvlFR2GtlNoRidgeNormNorm1Fam2StoryVery GoodAverageGableCompShgVinylSdVinylSdBrkFaceGdTAPConcGdTAAvGLQUnfGasAExYSBrkrGdTypTAAttchdRFnTATAYWDNormal
585.00000014115.00.0732.00.064.0796.0796.0566.00.01362.01.00.01.01.01.01.05.00.02.0480.040.030.00.0320.00.00.0700.010.0143000.031.029.031.015.01-1/2 STORY FINISHED ALL AGESRLIR1LvlInsideGtlMitchelNormNorm1Fam1.5FinAverageAverageGableCompShgVinylSdVinylSdNATATAWoodGdTANoGLQUnfGasAExYSBrkrTATypNAAttchdUnfTATAYWDNormal
675.00000010084.0186.01369.00.0317.01686.01694.00.00.01694.01.00.02.00.03.01.07.01.02.0636.0255.057.00.00.00.00.00.08.0307000.020.019.020.017.01-STORY 1946 & NEWER ALL STYLESRLRegLvlInsideGtlSomerstNormNorm1Fam1StoryVery GoodAverageGableCompShgVinylSdVinylSdStoneGdTAPConcExTAAvGLQUnfGasAExYSBrkrGdTypGdAttchdRFnTATAYWDNormal
770.04995810382.0240.0859.032.0216.01107.01107.0983.00.02090.01.00.02.01.03.01.07.02.02.0484.0235.0204.0228.00.00.00.0350.011.0200000.051.051.051.015.02-STORY 1946 & NEWERRLIR1LvlCornerGtlNWAmesPosNNorm1Fam2StoryGoodAbove AverageGableCompShgHdBoardHdBoardStoneTATACBlockGdTAMnALQBLQGasAExYSBrkrTATypTAAttchdRFnTATAYWDNormal
851.0000006120.00.00.00.0952.0952.01022.0752.00.01774.00.00.02.00.02.02.08.02.02.0468.090.00.0205.00.00.00.00.04.0129900.093.074.093.016.01-1/2 STORY FINISHED ALL AGESRMRegLvlInsideGtlOldTownArteryNorm1Fam1.5FinGoodAverageGableCompShgBrkFaceWd ShngNATATABrkTilTATANoUnfUnfGasAGdYFuseFTAMin1TADetchdUnfFaTAYWDAbnorml
950.0000007420.00.0851.00.0140.0991.01077.00.00.01077.01.00.01.00.02.02.05.02.01.0205.00.04.00.00.00.00.00.01.0118000.085.074.085.016.02 FAMILY CONVERSION - ALL STYLES AND AGESRLRegLvlCornerGtlBrkSideArteryArtery2fmCon1.5UnfAverageAbove AverageGableCompShgMetalSdMetalSdNATATABrkTilTATANoGLQUnfGasAExYSBrkrTATypTAAttchdRFnGdTAYWDNormal
LotFrontageLotAreaMasVnrAreaBsmtFinSF1BsmtFinSF2BsmtUnfSFTotalBsmtSF1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrTotRmsAbvGrdFireplacesGarageCarsGarageAreaWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldSalePriceYearBuilt_AgeYearRemodAdd_AgeGarageYrBlt_AgeYrSold_AgeMSSubClassMSZoningLotShapeLandContourLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinType2HeatingHeatingQCCentralAirElectricalKitchenQualFunctionalFireplaceQuGarageTypeGarageFinishGarageQualGarageCondPavedDriveSaleTypeSaleCondition
144921.01533.00.0553.00.077.0630.0630.00.00.0630.01.00.01.00.01.01.03.00.00.00.00.00.00.00.00.00.00.08.092000.054.054.045.49383618.0PUD - MULTILEVEL - INCL SPLIT LEV/FOYERRMRegLvlInsideGtlMeadowVNormNormTwnhsSFoyerAverageGoodGableCompShgCemntBdCmentBdNATATACBlockGdTAAvGLQUnfGasAExYSBrkrExTypNANANANANAYWDAbnorml
145060.09000.00.00.00.0896.0896.0896.0896.00.01792.00.00.02.02.04.02.08.00.00.00.032.045.00.00.00.00.00.09.0136000.050.050.045.49383615.0DUPLEX - ALL STYLES AND AGESRLRegLvlFR2GtlNAmesNormNormDuplex2StoryAverageAverageGableCompShgVinylSdVinylSdNATATACBlockGdTANoUnfUnfGasATAYSBrkrTATypNANANANANAYWDNormal
145178.09262.0194.00.00.01573.01573.01578.00.00.01578.00.00.02.00.03.01.07.01.03.0840.00.036.00.00.00.00.00.05.0287090.016.015.016.00000015.01-STORY 1946 & NEWER ALL STYLESRLRegLvlInsideGtlSomerstNormNorm1Fam1StoryVery GoodAverageGableCompShgCemntBdCmentBdStoneGdTAPConcGdTANoUnfUnfGasAExYSBrkrExTypGdAttchdFinTATAYNewPartial
145235.03675.080.0547.00.00.0547.01072.00.00.01072.01.00.01.00.02.01.05.00.02.0525.00.028.00.00.00.00.00.05.0145000.019.019.019.00000018.0PUD - MULTILEVEL - INCL SPLIT LEV/FOYERRMRegLvlInsideGtlEdwardsNormNormTwnhsESLvlAverageAverageGableCompShgVinylSdVinylSdBrkFaceTATAPConcGdTAGdGLQUnfGasAGdYSBrkrTATypNABasmentFinTATAYWDNormal
145390.017217.00.00.00.01140.01140.01140.00.00.01140.00.00.01.00.03.01.06.00.00.00.036.056.00.00.00.00.00.07.084500.018.018.045.49383618.01-STORY 1946 & NEWER ALL STYLESRLRegLvlInsideGtlMitchelNormNorm1Fam1StoryAverageAverageGableCompShgVinylSdVinylSdNATATAPConcGdTANoUnfUnfGasAExYSBrkrTATypNANANANANAYWDAbnorml
145462.07500.00.0410.00.0811.01221.01221.00.00.01221.01.00.02.00.02.01.06.00.02.0400.00.0113.00.00.00.00.00.010.0185000.020.019.020.00000015.01-STORY 1946 & NEWER ALL STYLESFVRegLvlInsideGtlSomerstNormNorm1Fam1StoryGoodAverageGableCompShgVinylSdVinylSdNAGdTAPConcGdTANoGLQUnfGasAExYSBrkrGdTypNAAttchdRFnTATAYWDNormal
145562.07917.00.00.00.0953.0953.0953.0694.00.01647.00.00.02.01.03.01.07.01.02.0460.00.040.00.00.00.00.00.08.0175000.025.024.025.00000017.02-STORY 1946 & NEWERRLRegLvlInsideGtlGilbertNormNorm1Fam2StoryAbove AverageAverageGableCompShgVinylSdVinylSdNATATAPConcGdTANoUnfUnfGasAExYSBrkrTATypTAAttchdRFnTATAYWDNormal
145685.013175.0119.0790.0163.0589.01542.02073.00.00.02073.01.00.02.00.03.01.07.02.02.0500.0349.00.00.00.00.00.00.02.0210000.046.036.046.00000014.01-STORY 1946 & NEWER ALL STYLESRLRegLvlInsideGtlNWAmesNormNorm1Fam1StoryAbove AverageAbove AverageGableCompShgPlywoodPlywoodStoneTATACBlockGdTANoALQRecGasATAYSBrkrTAMin1TAAttchdUnfTATAYWDNormal
145868.09717.00.049.01029.00.01078.01078.00.00.01078.01.00.01.00.02.01.05.00.01.0240.0366.00.0112.00.00.00.00.04.0142125.074.028.074.00000014.01-STORY 1946 & NEWER ALL STYLESRLRegLvlInsideGtlNAmesNormNorm1Fam1StoryAverageAbove AverageHipCompShgMetalSdMetalSdNATATACBlockTATAMnGLQRecGasAGdYFuseAGdTypNAAttchdUnfTATAYWDNormal
145975.09937.00.0830.0290.0136.01256.01256.00.00.01256.01.00.01.01.03.01.06.00.01.0276.0736.068.00.00.00.00.00.06.0147500.059.059.059.00000016.01-STORY 1946 & NEWER ALL STYLESRLRegLvlInsideGtlEdwardsNormNorm1Fam1StoryAverageAbove AverageGableCompShgHdBoardHdBoardNAGdTACBlockTATANoBLQLwQGasAGdYSBrkrTATypNAAttchdFinTATAYWDNormal